Abstract is missing.
- A Segmentation Network Based on 3D U-Net for Automatic Renal Cancer Structure Segmentation in CTA ImagesXin Weng, Zuquan Hu, Fan Yang. 3-8 [doi]
- Boundary-Aware Network for Kidney ParsingShishuai Hu, Zehui Liao, Yiwen Ye, Yong Xia 0001. 9-17 [doi]
- A CNN-Based Multi-stage Framework for Renal Multi-structure SegmentationYusheng Liu, Zhongchen Zhao, Lisheng Wang. 18-26 [doi]
- CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney SegmentationZhenyu Bu, Kaini Wang, Guangquan Zhou. 27-35 [doi]
- Automated 3D Segmentation of Renal Structures for Renal Cancer TreatmentMd Mahfuzur Rahman Siddiquee, Dong Yang 0005, Yufan He, Daguang Xu, Andriy Myronenko. 36-42 [doi]
- Ensembled Autoencoder Regularization for Multi-structure Segmentation for Kidney Cancer TreatmentDavid Jozef Hresko, Marek Kurej, Jakub Gazda, Peter Drotár. 43-51 [doi]
- Segmentation of Intra-operative Ultrasound Using Self-supervised Learning Based 3D-ResUnet Model with Deep SupervisionAbdul Qayyum, Moona Mazher, Steven A. Niederer, Imran Razzak. 55-62 [doi]
- Ultrasound Segmentation Using a 2D UNet with Bayesian Volumetric SupportAlistair Weld, Arjun Agrawal, Stamatia Giannarou. 63-68 [doi]
- Segmentation of Intraoperative 3D Ultrasound Images Using a Pyramidal Blur-Pooled 2D U-NetMostafa Sharifzadeh, Habib Benali, Hassan Rivaz. 69-75 [doi]
- Accurate Detection of Mediastinal Lesions with nnDetectionMichael Baumgartner 0001, Peter M. Full, Klaus H. Maier-Hein. 79-85 [doi]