The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index; 6 of the 18 sources its claims cite were checked for support.
3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch
Open accessAbstract
Abstract In image-guided spine surgery, robust three-dimensional to two-dimensional (3D–2D) registration of preoperative computed tomography (CT) and intraoperative radiographs can be challenged by the image content mismatch associated with the presence of surgical instrumentation and implants as well as soft-tissue resection or deformation. This work investigates image similarity metrics in 3D–2D registration offering improved robustness against mismatch, thereby improving performance and reducing or eliminating the need for manual masking. The performance of four gradient-based image similarity metrics (gradient information (GI), gradient correlation (GC), gradient information with linear scaling (GS), and gradient orientation (GO)) with a multi-start optimization strategy was evaluated in an institutional review board-approved retrospective clinical study using 51 preoperative CT images and 115 intraoperative mobile radiographs. Registrations were tested with and without polygonal masks as a function of the number of multistarts employed during optimization. Registration accuracy was evaluated in terms of the projection distance error (PDE) and assessment of failure modes (PDE > 30 mm) that could impede reliable vertebral level localization. With manual polygonal masking and 200 multistarts, the GC and GO metrics exhibited robust performance with 0% gross failures and median PDE < 6.4 mm (±4.4 mm interquartile range (IQR)) and a median runtime of 84 s (plus upwards of 1–2 min for manual masking). Excluding manual polygonal masks and decreasing the number of multistarts to 50 caused the GC-based registration to fail at a rate of >14%; however, GO maintained robustness with a 0% gross failure rate. Overall, the GI, GC, and GS metrics were susceptible to registration errors associated with content mismatch, but GO provided robust registration (median PDE = 5.5 mm, 2.6 mm IQR) without manual masking and with an improved runtime (29.3 s). The GO metric improved the registration accuracy and robustness in the presence of strong image content mismatch. This capability could offer valuable assistance and decision support in spine level localization in a manner consistent with clinical workflow.
Overview
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TL;DR
This study evaluated four gradient-based similarity metrics for 3D–2D CT-to-radiograph registration in spine surgery under image content mismatch. Gradient orientation (GO) was most robust, maintaining 0% gross failures without manual polygonal masking and with shorter runtime.
Key findings
- With manual polygonal masking and 200 multistarts, GC and GO achieved 0% gross failures and median projection distance error (PDE) < 6.4 mm (±4.4 mm IQR), with median runtime of 84 s plus 1–2 min for manual masking.
- Without manual polygonal masks and with 50 multistarts, GC failed at >14%, while GO maintained 0% gross failure, median PDE = 5.5 mm (2.6 mm IQR), and runtime = 29.3 s.
- GI and GS were susceptible to content mismatch; in the R(p, 200) configuration they had >16% failure, though GS improved gross failure rate over GI by approximately 50%. In R(r, 50), GC gross failure increased to 15%.
- GO had the largest median capture range in the superior–inferior direction: 375 mm ± 122 mm IQR, versus GI 192 mm ± 214 mm, GC 345 mm ± 185 mm, and GS 293 mm ± 180 mm.
Methods
- Institutional review board-approved retrospective clinical study using 51 preoperative CT images and 115 intraoperative mobile radiographs from thoracolumbar spine surgery; radiographs included 105 lateral and 10 anterior–posterior projections.
- Four gradient-based similarity metrics were tested: gradient information (GI), gradient correlation (GC), gradient information with linear scaling (GS), and gradient orientation (GO), using multistart optimization with CMA-ES.
- Registration performance was evaluated under three configurations—R(p, 200), R(p, 50), and R(r, 50)—using PDE, failure defined as PDE > 30 mm, runtime, objective-function search-space quality, and sensitivity to superior–inferior initialization.
Limitations
- The paper states that spine deformation is not specifically addressed by the rigid registration approach, and that ongoing clinical evaluation is needed.
- The paper states that manual polygonal masking is time-consuming, subjective, error-prone, and disruptive to workflow; GS and modified GC also require parameters that are non-trivial to determine automatically.
- Evident from the text: the study is retrospective and single-institution, uses mostly lateral radiographs, and relies on neuroradiologist-annotated vertebral centroids as reference truth.
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Claims the paper makes
34 Own result · 30 Own interpretation · 15 No basis given · 4 Partly supported · 2 Sources not found · 1 Supported · 1 Supported so far · 1 No full text of the sources · 1 Not checked yet · 1 Own method · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, the challenge due to content mismatch was mitigated by manually defined polygonal masks that excluded extraneous regions during similarity metric evaluation.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
What the sources say
- Not matchedcites Lo et al 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Otake et al 2015 sourceExperimental — accuracy not yet measured
The Patient study section (page unavailable) says a weighting mask can down-weight regions with deformation or instrumentation so they contribute less to the similarity metric. It says such masks could potentially be made manually, but does not describe polygonal masks or establish that manually defined masks were used to mitigate content mismatch in this study. (LLM, conf=0.85)
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In the paper
“The GI and GC metrics have been shown previously (Penney et al 1998, Pluim et al 2000, Otake et al 2012b) to be useful when handling partial content mismatch.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
What the sources say
- Not checked — no full text of the sourcecites Penney et al 1998 source
- Not checked — no full text of the sourcecites Pluim et al 2000 source
- Partly supportedcites Otake et al 2012b sourceExperimental — accuracy not yet measured
Methods §2.1 says GI is robust to image-content mismatches, including surgical tools visible in fluoroscopy but absent from the preoperative CT. The Introduction identifies GC as a local-intensity metric and describes that category as generally robust in 3D–2D registration, but the paper does not specifically establish GC’s usefulness for partial content mismatch. Page numbers were not provided in our copy. (LLM, conf=0.86)
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In the paper
“From the similarity metrics that have been previously investigated, gradient-based metrics have shown promise in 3D–2D image registration problems involving intraoperative radiographs and preoperative CT images (Penney et al 1998, Gendrin et al 2011, Otake et al 2012b, 2015).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
What the sources say
- Not checked — no full text of the sourcecites Penney et al 1998 source
- Supportedcites Gendrin et al 2011 source
The Introduction describes registration of intrainterventional 2D images with preinterventional 3D images. The Results and Discussion report accurate gradient-based registration of CT volumes to kV and MV x-ray images, supporting the claim that these metrics show promise for this type of problem. (LLM, conf=0.91)
- Not matchedcites Otake et al 2012b, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, previous work on vertebral level localization in spine surgery (Lo et al 2015, Otake et al 2015) showed promising results with GC similarity.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
What the sources say
- Not matchedcites Lo et al 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Otake et al 2015 source
The Methods (§2.2) state that the study used gradient correlation (GC) as its registration similarity metric. The Patient study (§3.3) reports approximately 1.13 mm average landmark projection error with 9-DOF registration in two spine-surgery patients, while the Discussion (§4) identifies vertebral localization in spine surgery as a potential application. These are promising preliminary results, rather than a definitive clinical validation of level localization. (LLM, conf=0.87)
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GO exhibited the best overall performance with a median capture range of 375 mm ± 122 mm IQR measured at the detector.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.3. Sensitivity to initialization/capture range
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In the paper
“Overall, the GI, GC, and GS metrics were susceptible to registration errors associated with content mismatch,”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > Abstract
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- The authors state this as an assumption: The transformation between preoperative CT and intraoperative radiograph can be modeled as a rigid 6D transformation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These data exhibited a substantial degree of realistic variation in imaging protocols/techniques, image quality, and imaging systems used for preoperative and intraoperative imaging.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.1. Clinical study
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In the paper
“For all the metrics, the space exhibits numerous false local optima associated with surgical instruments present in the radiograph (but not the CT) or the semiperiodic structure of the spine itself in the S–I direction.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.2. Quality of the objective function search space
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In the paper
“The GC and GO metrics maintained superior performance in comparison to GI and GS.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
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In the paper
“For the R(p, 200) configuration (i.e. 2D polygonal masks and 200 multistarts), both the GC and GO metrics exhibited an accurate and robust performance.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
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In the paper
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
What the sources say
- Not checked — no full text of the sourcecites Lowe 2004 source
- Partly supportedcites Dalal and Triggs 2005 sourceExperimental — accuracy not yet measured
The Abstract describes histograms of oriented gradients used for human detection, supporting the object-detection example attributed to Dalal and Triggs. It does not establish that gradient orientation was used on its own, independent of other gradient information. (LLM, conf=0.82)
- Not checked — no full text of the sourcecites De Nigris et al 2012 source
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Every source the paper cites for this claim was checked and supports it.
In the paper
“Considering an average thoracolumbar vertebral height (superior–inferior direction) of 22 mm (Busscher et al 2010)”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.4. Performance evaluation
What the sources say
- Supportedcites Busscher et al 2010 source
Table 3 (Results, Vertebral bodies; page not provided) reports human vertebral-body heights by thoracic and lumbar level. The values vary by level and measurement position but support 22 mm as an approximate thoracolumbar height; the paper does not give 22 mm as a single pooled mean. (LLM, conf=0.83)
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For the acquired radiographs, the magnification factor at the detector typically ranged from 1.4–1.6.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.4. Performance evaluation
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In the paper
“The other metrics demonstrated a fairly broad capture range as well: GI capture range = 192 mm ± 214 mm IQR; GC capture range = 345 mm ± 185 mm IQR; and GS capture range = 293 mm ± 180 mm IQR.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.3. Sensitivity to initialization/capture range
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In the paper
“The PDE following initialization ranged from 11.3 to 111.1 mm (median PDE = 43.1 mm, interquartile range 20.9 mm).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- The authors state this as an assumption: The transformation between preoperative CT and intraoperative radiograph can be modeled as a rigid 6D transformation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The GO metric, on the other hand, maintained geometric accuracy and robustness.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
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In the paper
“On the other hand, GC shows a more distinct optimum at the solution, but it also exhibits sharp local maxima attributed to strong gradient magnitudes far from the solution (surgical instruments).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.2. Quality of the objective function search space
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“While all metrics align corresponding square and circle elements at the desired solution, the mismatching triangle at the degenerate solution causes the GI, GC metrics to yield a larger relative contribution to the overall metric value.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
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In the paper
“but GO provided robust registration (median PDE = 5.5 mm, 2.6 mm IQR) without manual masking and with an improved runtime (29.3 s).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > Abstract
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It may state two separate claims as one.
- The authors state this as an assumption: The transformation between preoperative CT and intraoperative radiograph can be modeled as a rigid 6D transformation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The performance of the GI, GC, and GS metrics degraded significantly, e.g. the gross failure rate for GC increasing to 15%.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“For GI and GS, the peak at the solution is less distinct from surrounding local maxima, although GS appears improved over GI (consistent with improved robustness noted above).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.2. Quality of the objective function search space
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In the paper
“the average computation time ranged from 23–29 s for the S = 50 configurations, compared to 65–85 s for the S = 200 configuration.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
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In the paper
“Among distributions RGO(p, 200), RGO(p, 50), and RGO(r, 50), we found p-value >0.94 failing to reject the null hypothesis that the PDE distributions have the same median.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
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In the paper
“For the GO metric, the gradient magnitude values do not dominate the overall metric value, and the desired solution is reached wherein corresponding vertebrae gradients align in a manner that is robust against extraneous gradients and occlusions from instrumentation.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“The GO metric exhibits the most distinct global optimum at the solution and suppresses false maxima far from the solution.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.2. Quality of the objective function search space
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“With manual polygonal masking and 200 multistarts, the GC and GO metrics exhibited robust performance with 0% gross failures and median PDE < 6.4 mm (±4.4 mm interquartile range (IQR)) and a median runtime of 84 s (plus upwards of 1–2 min for manual masking).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > Abstract
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- The authors state this as an assumption: The transformation between preoperative CT and intraoperative radiograph can be modeled as a rigid 6D transformation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the RGO(r, 50) configuration outperformed all of the GI, GS, and GC configurations in terms of geometric accuracy, robustness, and run time”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“but compromises the robustness of the registration and results in an increasing failure rate for all of the similarity metrics.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“but such mm-scale variations in PDE in table 2 were not statistically significant (p = 0.52, 0.72, 0.10, and 0.07 for GI, GC, GS, and GO, respectively) and are likely not clinically significant with respect to localizing a particular vertebral body.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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In the paper
“Although the GS metric somewhat improved the registration performance compared to GI, it did not exhibit particularly strong robustness to failure (>16%) over all the tested configurations.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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In the paper
“In spine surgery, for example, the following sources of mismatch are common occurrences (as shown in figure 1) and present particular challenges to 3D–2D registration: (a) The presence of surgical instrumentation in the intraoperative radiograph (e.g. retractors, screws, implants, clips, etc); and (b) non-rigid anatomical deformation”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
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In the paper
“However, such an approach increases the computational burden and makes the registration algorithm susceptible to segmentation errors.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
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“GC can accommodate such differences up to a linear factor and therefore offers some degree of robustness against mismatch:”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
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“Such broad capture ranges are partly attributable to the similarity metric and perhaps more so to the robust optimization strategy involving multiple parallel searches from multiple initial seed points.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.3. Sensitivity to initialization/capture range
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“Increasing the number of multistarts (denoted S in figure 2) boosts the density of seed points positioned within the multidimensional space and therefore improves the robustness of finding the global optima at the cost of additional computation in performing parallel searches (roughly proportional to the number of multi-starts).”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
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“The min operator in GI assumes that anatomical structures produce a constant gradient magnitude in both DRRs and radiographs.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
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“Image gradients can be used to filter out low spatial frequency differences such as soft tissue (Penney et al 1998, Pluim et al 2000) and act as a high-pass filter to focus the registration on the bony anatomy clearly visible in x-ray images.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
What the sources say
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“The GO similarity metric thereby exhibits robust performance against content mismatch and enables the definition of a simple rectangular mask of the radiograph collimators to reduce or eliminate the need for polygonal masking.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“The GO metric improved the registration accuracy and robustness in the presence of strong image content mismatch.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > Abstract
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- The authors state this as an assumption: The transformation between preoperative CT and intraoperative radiograph can be modeled as a rigid 6D transformation.
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“the definition of manual polygonal masks is subject to user variability and tends to exclude regions that may be disadvantageous to registration.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“Manual/semi-automatic methods to mask out such extraneous (or missing) content are time consuming, user dependent, error prone, and disruptive to workflow.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
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“This capability could offer valuable assistance and decision support in spine level localization in a manner consistent with clinical workflow.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > Abstract
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“The definition of polygons to mask extraneous objects is a slow, subjective step that is error prone and limits the practical utility of image-based registration within streamlined workflow.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 1. Introduction
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“Content mismatch caused by extraneous objects and tissue deformation can create a challenging search space with multiple local optima.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.3. 3D–2D registration framework
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“For the GS metric, this effect is mitigated partly by the scaling parameter α; however, the exclusion of extraneous gradients requires estimating α to accurately match the corresponding gradients in the two images.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 3. Results > 3.1. Registration accuracy and robustness
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“The challenging search space illustrated in figure 6 shows multiple local maxima resulting from extraneous objects and stands to benefit from the multistart optimization strategy to improve robustness.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“Deformation of the spine is not specifically addressed in the current (rigid) registration approach, but the large clinical dataset did present realistic levels of anatomical deformation.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“Moreover, the corresponding increase in robustness has diminishing return due to the ‘curse of dimensionality’ in the 6D search space, whereas the computation time increases approximately linearly with the number of multistarts.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“We evaluated this parameter by computing the ratio of maximum gradient magnitudes observed between the two images; however, the parameter should be adjusted according to mismatch caused by strong gradient magnitudes. Automatic determination of the parameter for any given pair of images is non-trivial.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“Therefore, it is desirable for the registration framework to rely less upon the number of multistarts, an attribute exhibited with the incorporation of the GO similarity metric.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“These improvements better enable clinical translation of the registration algorithm”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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“Another drawback of the polygonal masking approach is that it requires the user to anticipate and identify problematic gradients in a radiograph with many subtleties.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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In the paper
“The small variations in PDE could be attributed to errors in the localization of the vertebrae centroid (expert reader truth definition), the stochasticity of the CMA-ES optimizer, and/or fundamental limitations arising from the use of similarity metrics as surrogate measures of registration accuracy during the optimization process.”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 4. Discussion and conclusions
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The authors state this as an assumption.
The transformation between preoperative CT and intraoperative radiograph can be modeled as a rigid 6D transformation.
“The registration is performed by optimizing the image similarity between the preoperative CT image and the intraoperative radiograph (I2:R2→R) in a rigid 6D transformation space.”
The paper relies on this without support.
Disparity in the superior–inferior (S–I) position is the most susceptible factor for level misidentification in spine registration.
“disparity in the S–I position is believed to be the most susceptible for level misidentification (e.g. registration errors resulting in label positions away from the true level by one or more levels in the S–I direction).”
The paper relies on this without support.
The clinical need is to identify the correct vertebral level within approximately 30 mm PDE.
“Given the clinical need to identify the correct vertebral level within approximately 30 mm PDE (i.e. within the correct vertebral body)”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.2. Image pre-processing and target definition
“minor axis 50 mm”
§ 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch > 2. Methods and materials > 2.2. Image pre-processing and target definition
“major axis 25 mm”
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · cited 1×
“…(GO): While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under…”
BackgroundHistograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · cited 2×
“Gradient orientation (GO): While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occ…”
BackgroundMethodThe CMA Evolution Strategy: A Comparing Review
Hansen, N. (2007) · Studies in fuzziness and soft computing · doi:10.1007/3-540-32494-1_4 · Cited by 1,907 · cited 1×
“To achieve robust performance under these conditions, the registration process incorporates a multistart optimization with the covariance matrix adaptation; evolution strategy (CMA-ES) reported by (Hansen 2006).”
MethodA comparison of similarity measures for use in 2-D-3-D medical image registration
Penney, G. P., Weese, J., Little, J. A., Desmedt, P., Hill, D. L. G., & hawkes, D. J. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.730403 · Cited by 782
Image registration by maximization of combined mutual information and gradient information
Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.876307 · Cited by 670
Towards a New Evolutionary Computation
Lozano, J. A., Larrañaga, P., Inza, I., & Bengoetxea, E. (2006) · Springer eBooks · doi:10.1007/3-540-32494-1 · Cited by 519
Automated model-based vertebra detection, identification, and segmentation in CT images
Klinder, T., Östermann, J., Ehm, M., et al. (2009) · Medical Image Analysis · doi:10.1016/j.media.2009.02.004 · Cited by 331 · cited 1×
“Labeling in CT can potentially be done automatically (Klinder et al 2009, Ma and Lu 2013), but in this study, a boardcertified neuroradiologist annotated the vertebral labels at the approximate centroid of each vertebral body.”
Comparative anatomical dimensions of the complete human and porcine spine
Busscher, I., Ploegmakers, J. J. W., Verkerke, G. J., & Veldhuizen, A. G. (2010) · European Spine Journal · doi:10.1007/s00586-010-1326-9 · Cited by 295
The Prevalence of Wrong Level Surgery Among Spine Surgeons
Mody, M. G., Nourbakhsh, A., Stahl, D. L., Gibbs, M., Alfawareh, M. D., & Garges, K. J. (2008) · Spine · doi:10.1097/brs.0b013e31816043d1 · Cited by 221 · cited 1×
“This could potentially decrease wrong-level errors, reported to occur in approximately 1 in 3110 spine surgery procedures (Mody et al 2008), and reduce the time and stress associated with resolving anatomical uncertainty.”
BackgroundIntensity-based 2-D-3-D registration of cerebral angiograms
Hipwell, J. H., Penney, G., McLaughlin, R. A., Rhode, K., Summers, P. E., Cox, T. C. S., Byrne, J. V., Noble, J. A., & Hawkes, D. J. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819283 · Cited by 146 · cited 2×
“…Metrics Validation Penney et al (1998) CT to projection x-ray in spine surgery NCC, entropy, MI, GC, PI, GD Phantom + simulated clinical images Hipwell et al (2003) Magnetic resonance angiography (MRA) to digital subtraction angiography (DSA) in neurointerventions NCC, GC, entropy, MI, PI, GD…”
Background
Cited by
Sorted by citations
Deep learning in medical image registration: a survey
Haskins, G., Kruger, U., & Yan, P. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01060-x · Cited by 704
“They were able to significantly outperform registration approaches that used a state-of-the-art similarity metric given by [24].”
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Miao, S., Piat, S., Fischer, P., Tuysuzoglu, A., Mewes, P., Mansi, T., & Liao, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11576 · Cited by 113
Colonoscopy 3D video dataset with paired depth from 2D-3D registration
Bobrow, T. L., Golhar, M., Vijayan, R. C., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102956 · Cited by 113
“Depth-based registrations were optimized using L 1 , L 2 , Normalized Cross Correlation (NCC), and Gradient Correlation (GC) loss functions (De Silva et al., 2016).”
MethodMultiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation
Liao, H., Lin, W.-A., Zhang, J., Zhang, J., Luo, J., & Zhou, S. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01292 · Cited by 66
Han, R., Uneri, A., Vijayan, R., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101917 · Cited by 64
“For example, image registration between preoperative CT and 2D fluoroscopy have been proposed for target localization and guidance in spine surgery (De Silva et al., 2016; Uneri et al., 2014).”
BackgroundSelf-citationMedical image registration using unsupervised deep neural network: A scoping literature review
Abbasi, S., Tavakoli, M., Boveiri, H. R., Mosleh‐Shirazi, M. A., Khayami, R., Khorasani, H., Javidan, R., & Mehdizadeh, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103444 · Cited by 63
“4 is vital procedure in different clinical application such as image-guided [5, 6], motion tracking [7], segmentation [8, 9], dose accumulation [10, 11, 12], image reconstruction [13, 14].”
BackgroundAtlas-based automatic planning and 3D–2D fluoroscopic guidance in pelvic trauma surgery
Han, R., Uneri, A., Silva, T. D., Ketcha, M. D., Goerres, J., Vogt, S. K., Kleinszig, G., Osgood, G. M., & Siewerdsen, J. H. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab1456 · Cited by 57
Self-citationAMNet: Adaptive multi-level network for deformable registration of 3D brain MR images
Che, T., Wang, X., Zhao, K., Zhao, Y., Zeng, D., Li, Q., Zheng, Y., Yang, N., Wang, J., & Li, S. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102740 · Cited by 52
Hajek, J., Unberath, M., Fotouhi, J., Bier, B., Lee, S. C., Osgood, G. M., Maier, A., Armand, M., & Navab, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_35 · Cited by 52
“the VT C describing the transformation from 3D image volumes to an intra-operatively acquired X-ray image. For pre-operative data, VT C can be estimated via image-based 3D/2D registration, e.g. as in [9,10]. If the Carm is CBCT capable and the 3D volume is acquired intra-procedurally, VT C is known and can be dened as…”
Method3D/2D model-to-image registration by imitation learning for cardiac procedures
Toth, D. F., Miao, S., Kurzendorfer, T., Rinaldi, C. A., Liao, R., Mansi, T., Rhode, K., & Mountney, P. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1774-y · Cited by 51
Uneri, A., Silva, T. D., Goerres, J., Jacobson, M. W., Ketcha, M. D., Reaungamornrat, S., Kleinszig, G., Vogt, S. K., Khanna, A. J., Osgood, G. M., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa62c5 · Cited by 42
Self-citationSilva, T. D., Chew, E. Y., Hotaling, N. A., & Cukras, C. A. (2020) · Biomedical Optics Express · doi:10.1364/boe.408573 · Cited by 42
Self-citationAn application independent review of multimodal 3D registration methods
Saiti, E., & Theoharis, T. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.07.012 · Cited by 42
“While the method achieves a high robustness and out-performs approaches that use the state-of-the-art similarity metric of [218] , registration accuracy remains challenging.”
MethodCorrection of patient motion in cone-beam CT using 3D–2D registration
Ouadah, S., Jacobson, M. W., Stayman, J. W., Ehtiati, T., Weiss, C. R., & Siewerdsen, J. H. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa9254 · Cited by 41
Self-citationKetcha, M. D., Silva, T. D., Uneri, A., Jacobson, M. W., Goerres, J., Kleinszig, G., Vogt, S. K., Wolinsky, J.-P., & Siewerdsen, J. H. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa6b3e · Cited by 37
“…provides reasonable registration accuracy overall, even in the presence of deformation (Otake et al 2013); however, recent clinical studies (De Silva et al 2016b) found a reduction in surgical confidence arising even from fairly small errors for which the label was still within cortical…”
BackgroundMethodResultInfluentialSelf-citationImage-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs
Nakao, M., Nakamura, M., & Matsuda, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3194517 · Cited by 37
“Optimization-based 2D/3D registration has been extensively researched over the past 20 years [6], [34]–[37].”
BackgroundRegistration of MRI to intraoperative radiographs for target localization in spinal interventions
Silva, T. D., Uneri, A., Ketcha, M. D., Reaungamornrat, S., Goerres, J., Jacobson, M. W., Vogt, S. K., Kleinszig, G., Khanna, A. J., Wolinsky, J.-P., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/62/2/684 · Cited by 29
Self-citationEsfandiari, H., Anglin, C., Guy, P., Street, J., Weidert, S., & Hodgson, A. J. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02024-x · Cited by 27
Han, R., Uneri, A., Ketcha, M. D., et al. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab843c · Cited by 28
Self-citationA Review of Keypoints’ Detection and Feature Description in Image Registration
Liu, C., Xu, J., & Wang, F. (2021) · Scientific Programming · doi:10.1155/2021/8509164 · Cited by 47
“To FAST, AOS (additive operator splitting) is exploited to accelerate calculation [54].”
MethodSimilarity attention-based CNN for robust 3D medical image registration
Zhu, F., Wang, S., Li, D., & Li, Q. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104403 · Cited by 26
Utility of the LevelCheck Algorithm for Decision Support in Vertebral Localization
Silva, T. D., Lo, S.-F. L., Aygün, N., et al. (2016) · Spine · doi:10.1097/brs.0000000000001589 · Cited by 25
Self-citationGoerres, J., Uneri, A., Jacobson, M. W., Ramsay, B., Silva, T. D., Ketcha, M. D., Han, R., Manbachi, A., Vogt, S. K., Kleinszig, G., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa954f · Cited by 24
Self-citationVirtual fluoroscopy for intraoperative C-arm positioning and radiation dose reduction
Silva, T. D., Punnoose, J., Uneri, A., et al. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.1.015005 · Cited by 24
“First, patient position is estimated by registering a single radiograph to the CT image via a 3D–2D image registration algorithm that has been shown in previous work to be robust over a wide variety of clinical imaging scenarios, including the presence of surgical instrumentation and soft-tissue deformation.(8,9,13,14…”
MethodSelf-citationSpineCloud: image analytics for predictive modeling of spine surgery outcomes
Silva, T. D., Vedula, S. S., Perdomo‐Pantoja, A., Vijayan, R. C., Doerr, S. A., Uneri, A., Han, R., Ketcha, M. D., Skolasky, R. L., Witham, T. F., et al. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.3.031502 · Cited by 23
“26,27 31 Example image-based features include automatic labeling of vertebral levels, global spinal alignment, endplate angles, intervertebral space, number of levels treated, and measurements of surgical instrumentation.”
BackgroundSelf-citationDong, G., Dai, J., Li, N., Zhang, C., He, W., Liu, L., Chan, Y., Li, Y., Xie, Y., & Liang, X. (2023) · Bioengineering · doi:10.3390/bioengineering10020144 · Cited by 24
C-arm positioning using virtual fluoroscopy for image-guided surgery
de Silva, T., Punnoose, J., Uneri, A., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2256028 · Cited by 23
“We utilize robust 3D-2D registration approaches[4]–[7] previously developed and validated in clinical images [8], [9].”
Self-citationCo-localized augmented human and X-ray observers in collaborative surgical ecosystem
Fotouhi, J., Unberath, M., Song, T., Hajek, J., Lee, S. C., Bier, B., Maier, A., Osgood, G. M., Armand, M., & Navab, N. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02035-8 · Cited by 19
Mi, J., Yin, W., Zhao, L., Chen, Y., Zhou, Y., & Feng, Q. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acd29d · Cited by 21
Robotic drill guide positioning using known-component 3D–2D image registration
Yi, T. Y., Ramchandran, V., Siewerdsen, J. H., et al. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.021212 · Cited by 19
Self-citationUnsupervised Cross-Modality Domain Adaptation Network for X-Ray to CT Registration
Zheng, S., Yang, X. N., Wang, Y., Ding, M., & Hou, W. W. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3135890 · Cited by 18
Metrics for Assessing the Similarity of Microwave Breast Imaging Scans of Healthy Volunteers
Lavoie, B. R., Bourqui, J., Fear, E. C., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2806878 · Cited by 18
“A number of metrics have been devised for quantifying the similarity of images [12]–[15].”
BackgroundZhang, X., Uneri, A., Wu, P., Ketcha, M. D., Jones, C. K., Huang, Y., Lo, S.-F. L., Helm, P. A., & Siewerdsen, J. H. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abde96 · Cited by 17
Self-citationGeng, H., Xiao, D., Yang, S., Fan, J., Fu, T., Lin, Y., Bai, Y., Ai, D., Song, H., Wang, Y., et al. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acede5 · Cited by 17
Morales‐Rodríguez, C., Vannini, A., Scanu, B., et al. (2025) · Current Forestry Reports · doi:10.1007/s40725-024-00237-1 · Cited by 17
Reynolds, T., Ma, Y., Kanawati, A. J., et al. (2022) · Investigative Radiology · doi:10.1097/rli.0000000000000885 · Cited by 16
Self-citationSiewerdsen, J. H., Levine, M., Silva, T. D., Vijayan, R. C., Doerr, S. A., Uneri, A., Vedula, S. S., Theodore, N., & Ketcha, M. D. (2019) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2513915 · Cited by 15
“, pedicle screws).(4,5) Robust, accurate, and automatic vertebrae centroid annotation thus poses a challenging problem for learning algorithms, especially in large data sets presenting a broad spectrum of realistic spinal pathology.”
BackgroundSelf-citationAutomatic masking for robust 3D-2D image registration in image-guided spine surgery
Ketcha, M. D., Silva, T. D., Uneri, A., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216913 · Cited by 14
Self-citationRealistic C-arm to pCT registration for vertebral localization in spine surgery
Naik, R. R., Anitha, H., Bhat, S. N., Ampar, N., & Kundangar, R. S. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02600-5 · Cited by 14
“Specifically, to improve robustness against image content mismatch, various gradient-based similarity metrics have been applied in vertebral centroid registration between CT and lateral plane X-ray images [19].”
BackgroundMulti-View Global 2D-3D Registration Based on Branch and Bound Algorithm
Pan, J., Min, Z., Zhang, A., Ma, H., & Meng, M. Q. ‐. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961379 · Cited by 13
Image-Guided Tethering Spine Surgery With Outcome Prediction Using Spatio-Temporal Dynamic Networks
Mandel, W., Oulbacha, R., Roy‐Beaudry, M., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3030741 · Cited by 13
“Alternatively, datadriven methods used global inter-connected representations of the spine [40] or unsupervised vertebral level identification techniques [41] in order to fuse intra-operative cone beam CT images with pre-operative plans.”
Method3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework
Huang, Y., Uneri, A., Jones, C. K., et al. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac07c7 · Cited by 13
Self-citationSilva, T. D., Cool, D. W., Yuan, J., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2703150 · Cited by 12
“and less computationally intensive than global methods with multiple initializations as in [26] and [27].”
MethodSelf-citationIntensity‐based 2D‐3D registration for an ACL reconstruction navigation system
Guo, N., Yang, B., Ji, X., et al. (2019) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2008 · Cited by 11
Kang, H., Jiang, H., Zhou, X., Yu, H., Hara, T., Fujita, H., & Yao, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2961268 · Cited by 11
“Commonly used cost functions include mean square error (MSE) [6], mutual information (MI) [7], normalized mutual information (NMI) [8], normalized cross-correlation (NCC) [9] and gradient correlation (GC) [10].”
BackgroundHCS-Net: Multi-level deformation strategy combined with quadruple attention for image registration
Ou, Z., Lu, X., & Gu, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107832 · Cited by 10
Lü, S., Jiang, P., Liang, Y., et al. (2024) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2024.3485629 · Cited by 9
“…GO represents the Gradient Orientation used as the image similarity measure function, which has been proven to be more robust in clinical images [37]; P ) denotes the projection of the patient’s preoperative 3D pelvic image X ( 3 d ) P using ray projection; and CAiCA 1 T represents the…”
Chen, M., Zhang, Z., Gu, S., & Kong, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446096 · Cited by 8
“In these traditional non-learning-based method [4, 5, 6], the 3D X-ray attenuation map is used to create the Digital Reconstructed Radiograph (DRR), which simulates the attenuation of virtual X-rays.”
MethodManbachi, A., Silva, T. D., Uneri, A., et al. (2018) · Annals of Biomedical Engineering · doi:10.1007/s10439-018-2099-2 · Cited by 8
Self-citationAgent-Based Methods for Medical Image Registration
Miao, S., & Liao, R. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-13969-8_16 · Cited by 9
Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery
An, Z., Ma, H., Liu, L., Wang, Y., Lu, H., Zhou, C., Xiong, R., & Hu, J. (2021) · Micromachines · doi:10.3390/mi12070844 · Cited by 7
“For comparison, we implement the state-of-the-art 2-D/3-D registration approach POINT2 [15], which can also be used as an initial pose estimator for optimization-based methods [9,31] called POINT2+opt.”
BackgroundMethodAnatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs
Bayat, A., Pace, D. F., Sekuboyina, A., Payer, C., Štern, D., Urschler, M., Kirschke, J. S., & Menze, B. H. (2022) · Tomography · doi:10.3390/tomography8010039 · Cited by 7
“Other approaches for this purpose introduce an automated 3D–2D spine registration algorithm [7,8], wherein authors suggest a multi-stage registration method by introducing a comparison metric for a CT projection and a radiograph.”
MethodVijayan, R. C., Sheth, N., Mekki, L., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac9e3c · Cited by 7
Self-citationChi, Y., Shen, C., Li, B., et al. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab4c0d · Cited by 6
Bindels, B. J. J., Weijers, R. A. M., van Mourik, M. S., et al. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02733-w · Cited by 6
Ushakiran, U., Naik, R. R., Anitha, H., & Bhat, S. N. (2023) · IEEE Access · doi:10.1109/access.2023.3286531 · Cited by 6
Abderezaei, J., Pionteck, A., Chopra, A., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44153-0_4 · Cited by 6
Ketcha, M. D., Silva, T. D., Han, R., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2907868 · Cited by 6
“examine 3D-2D registration scenarios, which previous studies [11], [12] showed to have better performance when using”
BackgroundSelf-citationJobidon-Lavergne, H., Kadoury, S., Knez, D., et al. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab1bfa · Cited by 6
Deformable registration of lateral cephalogram and cone‐beam computed tomography image
Zhang, Y., Qin, H., Li, P., Pei, Y., Guo, Y., Xu, T., & Zha, H. (2021) · Medical Physics · doi:10.1002/mp.15214 · Cited by 6
Contrastive learning based method for X-ray and CT registration under surgical equipment occlusion
Wang, X., Zhang, Z., Xu, S., Luo, X., Zhang, B., & Wu, X.-J. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108946 · Cited by 5
Fluoroscopic guidance of a surgical robot: pre-clinical evaluation in pelvic guidewire placement
Vijayan, R. C., Han, R., Wu, P., et al. (2021) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2582188 · Cited by 5
Self-citationChen, L., Zhang, X., He, Y., et al. (2021) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2283 · Cited by 5
“Silva et al.(29) studied the index of image similarity in 3D‐2D registration.”
BackgroundInfluentialFeature-based multimodal registration framework for vertebral pose estimation
Kiran, U., Bhat, S. N., Anitha, H., et al. (2023) · European Spine Journal · doi:10.1007/s00586-023-08054-z · Cited by 4
A new method to reconstruct intra-fractional prostate motion in volumetric modulated arc therapy
Chi, Y., Rezaeian, N. H., Shen, C., Zhou, Y., Lu, W., Yang, M., Hannan, R., & Jia, X. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa6e37 · Cited by 4
Drill-mounted video guidance for orthopaedic trauma surgery
Vagdargi, P., Sheth, N., Sisniega, A., et al. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.1.015002 · Cited by 4
“Real-time guidance is achieved by virtual overlay of the registered drill axis on fluoroscopy or in CT. Performance was evaluated in terms of target registration error (TRE), conformance within clinically relevant pelvic bone corridors, and runtime.”
MethodInfluentialSelf-citationImage-guided robotic k-wire placement for orthopaedic trauma surgery
Vijayan, R. C., Han, R., Wu, P., et al. (2020) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2549713 · Cited by 4
Self-citationSiewerdsen, J. H., Uneri, A., Zhang, X., et al. (2018) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2293874 · Cited by 3
“The objective function is solved using covariance matrix adaptation evolution strategy (CMA-ES), with registration of anatomy employing gradient orientation (GO) similarity for robustness to content mismatch, and component registration using gradient correlation (GC) to exploit high contrast edges.(11) A CBCT image ob…”
Self-citationSisniega, A., Hernandez, A. M., Aminololama‐Shakeri, S., et al. (2024) · Medical Physics · doi:10.1002/mp.17452 · Cited by 3
Self-citationUshakiran, U., Anitha, H., Bhat, S. N., et al. (2022) · International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) · doi:10.1109/icstcee56972.2022.10099932 · Cited by 3
“, [11] used gradient orientation similarity metric with CMA-ES”
MethodCollaboration calibration and three-dimensional localization in multi-view system
Feng, S., Wu, C., Zhang, Y., et al. (2018) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881418813778 · Cited by 3
Vijayan, R. C., Han, R., Wu, P., et al. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.3.035001 · Cited by 3
Self-citationJiang, Y., Pei, Y., Xu, T., Yuan, X., & Zha, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3456251 · Cited by 2
Preventing Wrong-Level Spine Surgery
Agolia, J. P., Robertson, S., Turel, K. E., & Kasper, E. M. (2024) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-031-61601-3_1 · Cited by 3
Kong, D., Hu, S., Zhang, W., Zhao, G., Bai, X., Wang, X., Gurure, D. M., Li, X., Li, X., & Wang, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107751 · Cited by 2
Siewerdsen, J. H., Ramsay, B., Silva, T. D., et al. (2018) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2293857 · Cited by 2
Self-citationPan, J., Min, Z., Zhang, A., Ma, H., & Meng, M. Q. ‐. (2021) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio54168.2021.9739622 · Cited by 2
“In Image-guided Intervention of Hip Joint [5], pre-operative 3D CT is implemented to provide the patient’s information to plan the surgery [6], [7].”
BackgroundEvaluation of Similarity Measure for Feature based 3D-2D Registration in Vertebral Pose Estimation
Ushakiran, U., Bhat, S. N., Naik, R. R., & Anitha, H. (2023) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet57972.2023.10169995 · Cited by 2
“, [6] evaluated the accuracy of the registration method in the presence of surgical instruments by comparing various gradient similarity measures.”
MethodRigid Shape Registration Based on Extended Hamiltonian Learning
Yi, J., Zhang, S., Cao, Y., et al. (2020) · Entropy · doi:10.3390/e22050539 · Cited by 2
Towards Automated Spine Mobility Quantification: A Locally Rigid CT to X-ray Registration Framework
Drobny, D., Ranzini, M. B. M., Isaac, A., Vercauteren, T. K., Sebastien Ourselin, A., Choi, D., & Marc Modat, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50120-4_7 · Cited by 2
Representing Ambiguity in Registration Problems with Conditional Invertible Neural NetworksPreprint
Trofimova, D., Adler, T. J., Kausch, L., Ardizzone, L., Maier-Hein, K. H., Köthe, U., Rother, C., & Maier‐Hein, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.08195 · Cited by 5
Collision Constraints and Diffeomorphisms: Dealing With Physics in Deformable Image Registration
Alscher, T., Petersen, J., Lauze, F., Erleben, K., & Darkner, S. (2025) · IEEE Access · doi:10.1109/access.2025.3588760 · Cited by 1
“Practical applications for image registration in the medical field incorporate atlas creation [5], surgery image guidance [6], and image fusion [7], among others.”
Real-time image-based 3D-2D registration for ultrasound-guided spinal interventions
Siewerdsen, J. H., Silva, T. D., Uneri, A., et al. (2018) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2293710 · Cited by 1
Self-citationSparse Branch-and-Bound 2D-3D Registration Based on Prior Correspondences of Dense Points
Pan, J., Min, Z., Shen, Y., Liu, T., & Meng, M. Q. ‐. (2022) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm54641.2022.9959548 · Cited by 1
Huang, Y., Zhang, X., Johnston, A., et al. (2023) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2654329 · Cited by 1
Self-citationVijayan, R. C., Venkataraman, K., Wei, J., et al. (2023) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2654481 · Cited by 1
Self-citationEvaluating similarity measure for multimodal 3D to 2D registration
Kiran, U., Naik, R. R., Bhat, S. N., & Anitha, H. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ace9e1 · Cited by 1
“To overcome the intensity difference between the multi-modal images, De Silva et al [10] employed a segmentation algorithm in the pre-processing step.”
MethodCompensation for Patient Movements in CBCT Imaging for Dental Applications
Ali, A. S. R. A., Sarti, C., & Landi, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43153-1_45 · Cited by 1
Vijayan, R. C., Sheth, N., Mekki, L., et al. (2022) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2612986 · Cited by 1
“A comparison of the objective functions (RMSD, NCC, GC, NGI, and GO) as shown in Figure 3a indicates that RMSD (PDE = 13.9 6.9 mm) and NCC (PDE = 13.0 5.9 mm) perform poorly for conventional local 3D- 2D lung registration, suffering from local minima presented by strong rib gradients.”
BackgroundInfluentialSelf-citationRegistration made easy -- standalone orthopedic navigation with HoloLensPreprint
Liebmann, F., Roner, S., von Atzigen, M., Wanivenhaus, F., Neuhaus, C., Spirig, J. M., Scaramuzza, D., Sutter, R., Snedeker, J. G., Farshad, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.06209 · Cited by 3
“measure which can be optimized in an iterative fashion. However, surgical tools, implants and resected soft tissue which are usually not present in preoperative images make this approach challenging [6]. DRR generation is computationally expensive and a balance between robustness and computational cost has to be found…”
BackgroundTechnical note: known-component registration for robotic drill guide positioning
Yi, T. Y., Ramchandran, V., Siewerdsen, J. H., et al. (2018) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2322408
Self-citationChaitanya, B. H. R., Anitha, H., Shyamasunder, N. B., et al. (2018) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2018.8554401
“But among the similarity metrics, GO has an advantage that both the instrumentation and vertebrae gradients contribute similar weights to the overall metric value thereby exhibiting stronger robustness to mismatch.[13].”
InfluentialLiao, H., Lin, W.-A., Zhang, J., Zhang, J., Luo, J., & Zhou, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03896
Ketcha, M. D. (2020)
Self-citationLung CT image registration based on end-to-end unsupervised learning
Zhu, Z., Ji, Y., & Wei, Y. (2021) · International Conference on Communication, Image and Signal Processing (CCISP) · doi:10.1109/ccisp52774.2021.9639353
An improved multi-resolution 2D/3D registration method
Cao, Y., He, F., Qu, F., Wang, T., Chen, Y., Shi, W., & Jiang, Z. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824315
A Review of Honey Application in Marinades Towards Hetero-Cyclic Amines (HCA) Formation
Mat, N. N. S., & Sarbon, N. M. (2022) · Advances in environmental engineering and green technologies book series · doi:10.4018/978-1-6684-6265-2.ch001
Topologically preserved registration of 3D CT images with deep networks
Liu, H., Gong, G., Zou, W., Hu, N., & Wang, J. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acb197
Registration: 2D/3D rigid registration
Liao, H., Zhou, S., & Luo, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824383-1.00014-9
Metadata
- Authors
- De Silva, T., Uneri, A., Ketcha, M. D., Reaungamornrat, S., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.-F., Wolinsky, J.-P., & Siewerdsen, J. H.
- Affiliations
- Johns Hopkins University
- Johns Hopkins University (US)
- Siemens (Germany) (DE)
- Johns Hopkins Medical Institute
- Corresponding author
- Siewerdsen, J H
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2016-03-18
- Volume
- 61
- Issue
- 8
- Pages
- 3009-3025
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- PMC
- PMC4915376
- OpenAlex
- W2329673985
- Semantic Scholar
- b79816585c14219cea6401f1f6b70af222d8b96a
- Cited by
- 107
- References
- 27
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 16 · 2022: 12 · 2021: 13 · 2020: 14 · 2019: 13 · 2018: 13 · 2017: 8 · 2016: 2
- FWCI
- 5.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- National Institutes of Health (R01-EB-017226)
- Siemens Healthcare
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Surgery › Spinal Fractures and Fixation Techniques
- Keywords
- image-guided spine surgery, image similarity measures, gradient direction, gradient correlation, gradient information, preoperative CT, multi-start optimization, surgical instrumentation, soft tissue deformation
- MeSH terms
- Humans, Retrospective Studies, Spinal Diseases, Tomography, X-Ray Computed, Imaging, Three-Dimensional, Surgery, Computer-Assisted
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