ArticleMedical image analysis2024
DeepSSM: A blueprint for image-to-shape deep learning models.
Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A Clinical Primer on Computer Vision.Calcified tissue international · 2026Review
- Anatomy-aware fusion attention network for high-precision prostate MRI segmentation.Medical physics · 2026Article
- Computational modelling for personalized transcatheter aortic valve replacement planning: a systematic review of complications and decision support.Frontiers in digital health · 2026Review
- MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images.Shape in medical imaging : International Workshop, ShapeMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. ShapeMI (Workshop) (2024 : Marrakech, Morocco) · 2025Article
- Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.Machine learning in medical imaging. MLMI (Workshop) · 2025Article
- Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images.Shape in medical imaging : International Workshop, ShapeMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. ShapeMI (Workshop) (2024 : Marrakech, Morocco) · 2025Article
- SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images.Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference) · 2024Article
- ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.