The paper introduces DPIF, a plug‑and‑play dual‑path interaction framework that can be integrated with any encoder‑decoder segmentation network. DPIF aligns cross‑view distributions using Sinkhorn optimal transport, decouples features via conditional mutual information to transfer knowledge within shared semantic spaces, and applies persistent homology‑based topological regularization to encode anatomical invariants. Experiments on a private echocardiography dataset and two public datasets show that DPIF‑enhanced models outperform single‑view baselines and achieve comparable performance to full‑data single‑view models while using only 40–60% of the data.
By Shuhuan Wang, Yuanyuan Cui, Pengju Wang, Huimin Guo, Ling Yue, Yin Gu, Yanchao Zhang, Wei Feng, Yunhe Liu, Wei Qian, Mingjun Xu, Shuang Liu, He Ma
arXiv:2608.24364v1 Announce Type: new
Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...
By Sebasti\'an Gonz\'alez, Karen Sanchez, Jos\'e M. Saavedra, Marcelo Pizarro, Bernard Ghanem
arXiv:2610.29090v1 Announce Type: new
Abstract: Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, th...
By Derong Yu, Guoyan Zheng
arXiv:2608.30844v1 Announce Type: cross
Abstract: Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to spa...
By Xinglong Liang, Chunyao Lu, Tianyu Zhang, Jiaju Huang, Tao Tan, Yunchao Yin, Lishan Cai
Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, their heavy reliance on large-scale image data rais...
arXiv:2505.03380v2 Announce Type: replace
Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
By Haonan Wang, Jiaji Mao, Lehan Wang, Qixiang Zhang, Marawan Elbatel, Yi Qin, Huijun Hu, Baoxun Li, Wenhui Deng, Weifeng Qin, Hongrui Li, Jialin Liang, Jun Shen, Xiaomeng Li
InstEditSeg is a generative framework that treats medical segmentation as an instruction-driven image editing task. Instead of producing binary masks, it renders a color-coded overlay on the original image guided by textual instructions, leveraging latent diffusion models to align with natural image distributions and reduce domain gaps. The method incorporates a DINOv3 visual encoder and a multi-scale feature pyramid fused into the diffusion U‑Net, and uses a dual‑branch classifier‑free guidance strategy to lower inference cost, achieving competitive accuracy on polyp and skin lesion datasets while improving cross‑domain generalization and multi‑lesion segmentation.
By Ziquan Liu, Zhewei Zhu, Xuyang Shi
arXiv:2610.06938v1 Announce Type: new
Abstract: Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed...
By Bangwei Guo, Yunhe Gao, Meng Ye, Yang Zhou, Difei Gu, Guoning Zhang, Leon Axel, Dimitris Metaxas
The study explores how adding anatomical priors and active learning can improve the accuracy of deep learning models for segmenting the Clinical Target Volume (CTV) in gastric cancer radiotherapy. Using 100 retrospective CT scans, an nnU‑Net model trained on 10 expert‑contoured cases was enhanced with voxel‑wise anatomical prior maps and iterative active learning over four rounds. The combined approach raised the mean Dice Similarity Coefficient from 0.84 to 0.87, demonstrating that both techniques individually and together improve segmentation performance and generalizability.
By Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway
AnyCXR is a unified framework that performs multi-organ segmentation on chest X-rays taken at any projection angle using only synthetic data. It employs a Multi-stage Domain Randomization engine to generate over 100,000 anatomically accurate synthetic radiographs from 3D CT volumes, and a Conditional Joint Annotation Regularization strategy to learn from partial, imperfect labels by enforcing anatomical consistency in a latent space. Trained solely on synthetic data, AnyCXR achieves strong zero-shot generalization on real-world datasets, accurately delineating 54 anatomical structures across PA, lateral, and oblique views, and supports downstream clinical tasks such as cardiothoracic ratio estimation, spine curvature assessment, and disease classification.
By Zifei Dong, Wenjie Wu, Jinkui Hao, Tianqi Chen, Ziqiao Weng, Bo Zhou
arXiv:2608. 07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration.
By Jia Wang, Jiaming Cai, Zunying Hu, Zhanjie Wu, Jinyuan Liu, Hua Cheng, Yun Peng
arXiv:2608. 13690v1 Announce Type: cross Abstract: Medical image segmentation is still largely treated as a vision-only problem, although clinical interpretation often relies on textual knowledge of anatomy, location, appearance, and surrounding context.
By Rafi Ibn Sultan, Hui Zhu, Chengyin Li, Dongxiao Zhu