SlicerMorph: An open and extensible platform to retrieve, visualize and analyse 3D morphology
Large‐scale digitization projects such as# ScanAllFishes and oVert are generating high‐
resolution microCT scans of vertebrates by the thousands. Data from these projects are …
resolution microCT scans of vertebrates by the thousands. Data from these projects are …
DeepSSM: a deep learning framework for statistical shape modeling from raw images
Statistical shape modeling is an important tool to characterize variation in anatomical
morphology. Typical shapes of interest are measured using 3D imaging and a subsequent …
morphology. Typical shapes of interest are measured using 3D imaging and a subsequent …
[HTML][HTML] Enabling supra-aortic vessels inclusion in statistical shape models of the aorta: a novel non-rigid registration method
MA Scarpolini, M Mazzoli, S Celi - Frontiers in Physiology, 2023 - frontiersin.org
Statistical Shape Models (SSMs) are well-established tools for assessing the variability of
3D geometry and for broadening a limited set of shapes. They are widely used in medical …
3D geometry and for broadening a limited set of shapes. They are widely used in medical …
From images to probabilistic anatomical shapes: A deep variational bottleneck approach
J Adams, S Elhabian - … Conference on Medical Image Computing and …, 2022 - Springer
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for
detecting pathology, diagnosing disease, and conducting population-level morphology …
detecting pathology, diagnosing disease, and conducting population-level morphology …
Morphologic analysis of the subtalar joint using statistical shape modeling
N Krähenbühl, AL Lenz, RJ Lisonbee… - Journal of …, 2020 - Wiley Online Library
Weightbearing computed tomography (WBCT) enables visualization of the foot and ankle as
patients stand under load. Clinical measurements of WBCT images are generally limited to …
patients stand under load. Clinical measurements of WBCT images are generally limited to …
Uncertain-deepssm: From images to probabilistic shape models
Statistical shape modeling (SSM) has recently taken advantage of advances in deep
learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy …
learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy …
Quantifying the severity of metopic craniosynostosis using unsupervised machine learning
EE Anstadt, W Tao, E Guo, L Dvoracek… - Plastic and …, 2023 - journals.lww.com
Background: Quantifying the severity of head shape deformity and establishing a threshold
for operative intervention remains challenging in patients with metopic craniosynostosis …
for operative intervention remains challenging in patients with metopic craniosynostosis …
A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images
J Schmid, L Assassi, C Chênes - European radiology experimental, 2023 - Springer
Background The collection and annotation of medical images are hindered by data scarcity,
privacy, and ethical reasons or limited resources, negatively affecting deep learning …
privacy, and ethical reasons or limited resources, negatively affecting deep learning …
Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach
Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of
many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive …
many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive …
Learning spatiotemporal statistical shape models for non-linear dynamic anatomies
Numerous clinical investigations require understanding changes in anatomical shape over
time, such as in dynamic organ cycle characterization or longitudinal analyses (eg, for …
time, such as in dynamic organ cycle characterization or longitudinal analyses (eg, for …