脳転移のセグメンテーションのためのカリキュラム学習を用いた特徴誘導型注意ネットワーク（FANCL）
本稿では、脳MRI画像における脳転移のセグメンテーション精度向上のため、新規深層学習モデル「FANCL」を提案する。FANCLは、従来のCNNベースの手法に、特徴誘導型注意機構とボクセルレベルカリキュラム学習戦略を導入することで、特に小腫瘍のセグメンテーション精度を大幅に向上させる。
SAM支援型整合性正則化による半教師あり医療画像セグメンテーションの強化：SemiSAM
本稿では、医療画像セグメンテーションにおける教師データ不足問題に取り組むため、Segment Anything Model (SAM) を既存の半教師あり学習フレームワークに統合した、SemiSAMと呼ばれる新規手法を提案する。
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.
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