arXiv Computer Vision
Sep 7

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

The paper introduces a Deep Active Contour and Mean Curvature (DACMC) loss function for medical image segmentation. By incorporating mean curvature as a geometric constraint and approximating it with a convolution kernel, the method aims to improve the geometric characterization of segmented regions. Experiments on liver and spleen datasets show that DACMC achieves new state‑of‑the‑art performance across several segmentation benchmarks.

By Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng, Ze-wen Li, Yan-zhe Hou
arXiv Machine Learning
Aug 27

Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

The paper introduces an unsupervised approach to medical image segmentation by training a Denoising Diffusion Probabilistic Model (DDPM) on 21 unlabeled abdominal CT scans to learn anatomical features. The encoder weights from the DDPM are transferred to a U‑Net for downstream segmentation on the BTCV multi‑organ dataset, resulting in a significant Dice score improvement for liver segmentation from 0.75 to 0.93. In low‑data regimes, diffusion‑pretrained models retain robust performance, achieving high Dice scores even with only 10% of labeled data.

By Akshat G, Divyansh Gupta, Shaleen Bhatnagar, Shilpa Ankalaki, Tusar Kanti Mishra