TL;DR
Inspired by traditional probabilistic atlases, PCMap is an average anatomical map generated from all initial segmentations, intended to enforce structural consistency, which increased shape regularity and reduced interslice discontinuities but did not consistently improve voxel-wise accuracy.
A modality-routed 3D cardiac segmentation pipeline that combines TotalSegmentator-initialized nnU-Netv2 models with site-characterized, label-preserving appearance augmentation is proposed, suggesting that site-motivated appearance augmentation is a practical strategy for improving cross-site robustness in limited-data...
This work presents BrainIAC (Brain lesion Interactive Adaptive Continuously learning segmentation), a unified framework that integrates a multi-modal backbone network trained to segment multiple types of brain lesions and handle heterogeneous sets of modalities via zero-filling and random modality dropping.
Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology. While deep learning approaches for 3D curvilinear segmentation have been proposed, they are often tailored to specific...
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
In comparison to the current state-of-the-art deep learning-based segmentation models, the proposed system surpasses them, highlighting its potential and reliability in precisely segmenting liver regions in CT volumes.
Using unlabeled images for diffusion-based pretraining successfully embeds robust anatomical features prior to human supervision, transforming U-Nets into anatomy-aware systems.
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