1 code implementation • 28 Jun 2024 • Wei Li, Jingyang Zhang, Pheng-Ann Heng, Lixu Gu
Generalist segmentation models are increasingly favored for diverse tasks involving various objects from different image sources.
2 code implementations • 7 Sep 2023 • Ziyan Huang, Zhongying Deng, Jin Ye, Haoyu Wang, Yanzhou Su, Tianbin Li, Hui Sun, Junlong Cheng, Jianpin Chen, Junjun He, Yun Gu, Shaoting Zhang, Lixu Gu, Yu Qiao
To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.
no code implementations • 13 Apr 2023 • Ziyan Huang, Haoyu Wang, Zhongying Deng, Jin Ye, Yanzhou Su, Hui Sun, Junjun He, Yun Gu, Lixu Gu, Shaoting Zhang, Yu Qiao
However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions.
no code implementations • 26 Sep 2021 • Zijie Chen, Cheng Li, Junjun He, Jin Ye, Diping Song, Shanshan Wang, Lixu Gu, Yu Qiao
An essential step of RT planning is the accurate segmentation of various organs-at-risks (OARs) in HaN CT images.
no code implementations • 26 Sep 2021 • Junjun He, Jin Ye, Cheng Li, Diping Song, Wanli Chen, Shanshan Wang, Lixu Gu, Yu Qiao
Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images.
no code implementations • 6 May 2021 • Jingyang Zhang, Ran Gu, Guotai Wang, Hongzhi Xie, Lixu Gu
To solve this problem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA) which requires only limited annotations for coronary arteries in XAs.
no code implementations • 27 May 2020 • Jingyang Zhang, Guotai Wang, Hongzhi Xie, Shuyang Zhang, Ning Huang, Shaoting Zhang, Lixu Gu
The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in a large number of training images, which is extremely labor-intensive especially for complex coronary trees.
no code implementations • 27 Sep 2018 • Yu Zhao, Zhenhui Shi, Jingyang Zhang, Dong Chen, Lixu Gu
The proposed method serves as a heuristic means to select high-value samples of high scalability and generality and is implemented through a three-step process: (1) the transformation of the sample selection to sample ranking and scoring, (2) the computation of the self-adaptive weights of each criterion, and (3) the weighted aggregation of each sample rank list.