The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Semantic segmentation is an essential component of medical image analysis research, with
recent deep learning algorithms offering out-of-the-box applicability across diverse datasets …

Anatomical shape analysis is pivotal in clinical research and hypothesis testing, where the
relationship between form and function is paramount. Correspondence-based statistical …

Supervised methods for 3D anatomy segmentation demonstrate superior performance but
are often limited by the availability of annotated data. This limitation has led to a growing …

Clinically deployed deep learning-based segmentation models are known to fail on data
outside of their training distributions. While clinicians review the segmentations, these …

Even after extensive efforts to enhance our understanding of materials, modeling, and
system processes, uncertainty continues to be an inevitable factor that impacts system …

Statistical shape modeling (SSM) is emerging as an important tool in medical image
analysis, allowing for population-based quantitative evaluation of morphometrics. SSM …

In 3D anatomy segmentation, fully supervised methods are recognized for their precision but
are constrained by the limited availability of detailed annotated datasets. This has catalyzed …