Marginal space deep learning: efficient architecture for volumetric image parsing
Abstract
Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.
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The current application of deep learning in medical US analysis is a growing trend supported by progress in the field.
“Furthermore, the current application of deep learning in medical US analysis is a growing trend that is supported by progress in this field [23,52].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [52]
MSDL demonstrated efficiency and robustness in 3D detection and segmentation of the aortic valve, with significant improvement up to 42.5% over the state of the art.
“The results on a large 3D transesophageal echocardiogram image dataset demonstrated the efficiency and robustness of the MSDL in the 3D detection and segmentation task of the aortic valve; it showed a significant improvement of up to 42.5% over the state of the art.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [52]
Using only the CPU, the aortic valve can be successfully segmented in less than one second with higher accuracy than the original MSL.
“By only using the central processing unit (CPU), the aortic valve can be successfully segmented in less than one second with higher accuracy than the original MSL.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [52]
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- Authors
- Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Siemens (United States) (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1217-1228
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2318872361
- Cited by
- 142
- References
- 49
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 7 · 2022: 13 · 2021: 29 · 2020: 12 · 2019: 31 · 2018: 14 · 2017: 18 · 2016: 8
- FWCI
- 16.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- medical image segmentation, boundary delineation, active shape model, ultrasonography
- MeSH terms
- Machine Learning, Algorithms, Aortic Valve, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Databases, Factual, Neural Networks, Computer, Echocardiography, Transesophageal
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