Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks
Open accessAbstract
Background and objective The spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Delimiting these OARs take a lot of time from medical specialists, plus the fact that involves a large team of professionals. Moreover, this task made slice-by-slice becomes an exhaustive and consequently subject to errors, especially in organs such as the spinal cord, which extend through several slices of the CT and requires precise segmentation. Thus, we propose, in this work, a computational methodology capable of detecting spinal cord in planning CT images.Methods The techniques highlighted in this methodology are adaptive template matching for initial segmentation, intrinsic manifold simple linear iterative clustering (IMSLIC) for candidate segmentation and convolutional neural networks (CNN) for candidate classification, that consists of four steps: (1) images acquisition, (2) initial segmentation, (3) candidates segmentation and (4) candidates classification.Results The methodology was applied on 36 planning CT images provided by The Cancer Imaging Archive, and achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images, without any false positives reduction technique, in detection of spinal cord.Conclusions It is demonstrated the feasibility of the analysis of planning CT images using IMSLIC and convolutional neural network techniques to achieve success in detection of spinal cord regions.
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Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error.
“Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error [9–17].”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [15]
Deep learning is increasingly used in medical imaging for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.
“In medical imaging, deep learning is increasingly used for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [15]
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- Authors
- Diniz, J. O. B., Diniz, P. H. B., Valente, T. L. A., Silva, A. C., & Paiva, A. C.
- Affiliations
- Universidade Federal do Maranhão (BR)
- Pontifícia Universidade Católica do Rio de Janeiro (BR)
- Corresponding author
- Diniz, João Otávio Bandeira
- ORCID
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 170
- Pages
- 53-67
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2911068672
- Cited by
- 34
- References
- 49
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 6 · 2021: 9 · 2020: 6 · 2019: 3
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Keywords
- radiotherapy treatment planning, organ-at-risk segmentation, adaptive template matching, simple linear iterative clustering, convolutional neural networks, CT image segmentation, organ-at-risk delineation
- MeSH terms
- Humans, Image Processing, Computer-Assisted, Quality of Health Care, Spinal Cord, Spinal Cord Injuries, Tomography, X-Ray Computed, Neural Networks, Computer
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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