no code implementations • 24 Apr 2024 • Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh, Tassilo Wald, Maximilian R. Rokuss, Maximilian Zenk, Maximilian Fischer, Michael Baumgartner, Fabian Isensee, Klaus H. Maier-Hein
This limitation leads to false predictions when applied to body regions beyond the FOV of the training data.
1 code implementation • 15 Apr 2024 • Fabian Isensee, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus Maier-Hein, Paul F. Jaeger
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
no code implementations • 3 Apr 2024 • Yannick Kirchhoff, Maximilian R. Rokuss, Saikat Roy, Balint Kovacs, Constantin Ulrich, Tassilo Wald, Maximilian Zenk, Philipp Vollmuth, Jens Kleesiek, Fabian Isensee, Klaus Maier-Hein
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision.
1 code implementation • 15 Dec 2023 • Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
no code implementations • 14 Sep 2023 • Gregor Koehler, Tassilo Wald, Constantin Ulrich, David Zimmerer, Paul F. Jaeger, Jörg K. H. Franke, Simon Kohl, Fabian Isensee, Klaus H. Maier-Hein
Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.
no code implementations • 5 Jul 2023 • Tassilo Wald, Constantin Ulrich, Fabian Isensee, David Zimmerer, Gregor Koehler, Michael Baumgartner, Klaus H. Maier-Hein
Given an ensemble of independently trained models, this results in correlated predictions and common failure modes.
no code implementations • 9 Apr 2023 • Saikat Roy, Gregor Koehler, Michael Baumgartner, Constantin Ulrich, Jens Petersen, Fabian Isensee, Klaus Maier-Hein
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation.
1 code implementation • 25 Mar 2023 • Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner, Klaus H. Maier-Hein
Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance.
1 code implementation • 17 Mar 2023 • Saikat Roy, Gregor Koehler, Constantin Ulrich, Michael Baumgartner, Jens Petersen, Fabian Isensee, Paul F. Jaeger, Klaus Maier-Hein
This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.
Ranked #1 on Medical Image Segmentation on AMOS
no code implementations • 23 Aug 2022 • Fabian Isensee, Constantin Ulrich, Tassilo Wald, Klaus H. Maier-Hein
Semantic segmentation is one of the most popular research areas in medical image computing.