arXiv subjects

Janmesh Ukey

Publications and source records attributed to Janmesh Ukey.

2 recordsLinked to original sources

arXiv2024

On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling

Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphology-based diagnostics and surgical planning. However, the effectiveness of SSMs is often constrained by the necessity for expert-driven manual segmentation, a time-intensive and expensive process that restricts their broader utility. While deep learning approaches offer a potential workaround by directly estimating SSMs from unsegmented images, they merely shift the burden. Although these models do not require segmentation during deployment, they still fail to address the challenge of acquiring the manual annotations needed for training, particularly in resource-limited settings. Semi-supervised models for anatomy segmentation present a logical solution to the annotation burden. However, the lack of established guidelines leaves end-users uncertain about the actual effectiveness of these approaches for the downstream task of constructing SSMs. In this study, we bridge this gap by systematically evaluating semi-supervised methods as viable alternatives to manual segmentation. By applying these methods under low-annotation settings and utilizing the predicted segmentations for SSM generation, we establish a comprehensive new performance benchmark. Our findings reveal a clear divide in performance: while certain methods yield noisy segmentations that degrade SSM quality, others accurately capture the population's modes of variation comparable to those obtained from manual-segmentation SSMs despite a 60-80% reduction in manual annotation requirements.

cs.CV↗

arXiv2024

MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images

Statistical Shape Modeling (SSM) effectively analyzes anatomical variations within populations but is limited by the need for manual localization and segmentation, which relies on scarce medical expertise. Recent advances in deep learning have provided a promising approach that automatically generates statistical representations (as point distribution models or PDMs) from unsegmented images. Once trained, these deep learning-based models eliminate the need for manual segmentation for new subjects. Most deep learning methods still require manual pre-alignment of image volumes and bounding box specification around the target anatomy, leading to a partially manual inference process. Recent approaches facilitate anatomy localization but only estimate population-level statistical representations and cannot directly delineate anatomy in images. Additionally, they are limited to modeling a single anatomy. We introduce MASSM, a novel end-to-end deep learning framework that simultaneously localizes multiple anatomies, estimates population-level statistical representations, and delineates shape representations directly in image space. Our results show that MASSM, which delineates anatomy in image space and handles multiple anatomies through a multitask network, provides superior shape information compared to segmentation networks for medical imaging tasks. Estimating Statistical Shape Models (SSM) is a stronger task than segmentation, as it encodes a more robust statistical prior for the objects to be detected and delineated. MASSM allows for more accurate and comprehensive shape representations, surpassing the capabilities of traditional pixel-wise segmentation.

cs.CV↗