Leveraging Temporal Differences for Improved Longitudinal Segmentation of Multiple Sclerosis Lesions
Incorporating explicit architectural bias to emphasize temporal differences between baseline and follow-up scans significantly enhances the performance of longitudinal multiple sclerosis lesion segmentation compared to state-of-the-art single timepoint and existing longitudinal methods.
Monte Carlo-guided Interpolation Consistency Segment Anything Model for Semi-Supervised Prostate Zone Segmentation
A Monte Carlo-guided interpolation consistency-based framework for segmenting 2D MR images of the prostate region, which improves the generalization ability of the Segment Anything Model (SAM) through semi-supervised learning.
One-Prompt Segmentation: A Versatile Approach for Universal Medical Image Segmentation
One-Prompt Segmentation combines the strengths of one-shot and interactive segmentation methods to enable zero-shot generalization across diverse medical imaging tasks, requiring only a single prompted sample during inference.
Test-Time Adaptation with SaLIP: A Cascade of Segment Anything Model and CLIP for Zero-shot Medical Image Segmentation
SaLIP, a unified framework that leverages the combined capabilities of the Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to perform zero-shot organ segmentation in medical images, without relying on domain expertise or annotated data for prompt engineering.
Efficient Connectivity-Preserving Segmentation of Thin Tubular Structures with Skeleton Recall Loss
Skeleton Recall Loss is a novel loss function that effectively preserves the connectivity of thin tubular structures in segmentation tasks, while being computationally efficient and compatible with multi-class problems.
Semi-Mamba-UNet: A Pixel-Level Contrastive and Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation
This paper introduces the Semi-Mamba-UNet, a novel framework that integrates a purely visual mamba-based U-Shape Encoder-Decoder architecture with a conventional CNN-based UNet into a Semi-Supervised Learning (SSL) framework, leveraging both networks to simultaneously generate pseudo labels and cross supervise each other on the pixel level.
U-Net v2: A Novel Encoder-Decoder Architecture with Efficient Skip Connections for Improved Medical Image Segmentation
A new U-Net variant, U-Net v2, is introduced that features a novel and straightforward design of skip connections to explicitly integrate semantic information from higher-level features and finer details from lower-level features into feature maps at each level, leading to improved medical image segmentation performance.
AgileFormer: A Spatially Agile Transformer UNet for Efficient Medical Image Segmentation
The core message of this paper is to introduce a novel spatially agile transformer UNet architecture, termed AgileFormer, that systematically incorporates deformable patch embedding, spatially dynamic self-attention, and multi-scale deformable positional encoding to effectively capture diverse target objects in medical image segmentation tasks.
UltraLight Vision Mamba UNet: A Highly Efficient Model for Skin Lesion Segmentation with Significantly Reduced Parameters
The proposed UltraLight Vision Mamba UNet (UltraLight VM-UNet) is a highly efficient model for skin lesion segmentation, achieving excellent performance with only 0.049M parameters and 0.060 GFLOPs, which is significantly lower than existing lightweight Vision Mamba models.
FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation
Medical image segmentation foundation models can be effectively deployed in a federated learning framework, maintaining performance and privacy.
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