paper-with-me

Papers

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

2023-07-05 · Nicholas Heller, Fabian Isensee, Dasha Trofimova, Resha Tejpaul, Zhongchen Zhao, Huai Chen, Lisheng Wang, Alex Golts, Daniel Khapun, Daniel Shats, Yoel Shoshan, Flora Gilboa-Solomon, Yasmeen George, Xi Yang, Jianpeng Zhang, Jing Zhang, Yong Xia, Mengran Wu, Zhiyang Liu, Ed Walczak, Sean McSweeney, Ranveer Vasdev, Chris Hornung, Rafat Solaiman, Jamee Schoephoerster, Bailey Abernathy, David Wu, Safa Abdulkadir, Ben Byun, Justice Spriggs, Griffin Struyk, Alexandra Austin, Ben Simpson, Michael Hagstrom, Sierra Virnig, John French, Nitin Venkatesh, Sarah Chan, Keenan Moore, Anna Jacobsen, Susan Austin, Mark Austin, Subodh Regmi, Nikolaos Papanikolopoulos, Christopher Weight

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed, in addition to a larger dataset. A novel annotation method was used to collect three separate annotations for each region of interest, and these annotations were performed in a fully transparent setting using a web-based annotation tool. Further, the KiTS21 test set was collected from an outside institution, challenging participants to develop methods that generalize well to new populations. Nonetheless, the top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance. An in-depth meta-analysis is presented describing which methods were used and how they faired on the leaderboard, as well as the characteristics of which cases generally saw good performance, and which did not. Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.

📄 PDF Abstract BibTeX arXiv:2307.01984

Code (1)

neheller/kits21 공식 구현

Tasks

SegmentationSemantic SegmentationTumor Segmentation

Similar Papers 제목 키워드 기반

Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks

2017-12-19 · Marzieh Haghighi, Simon K. Warfield, Sila Kurugol

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in thi…

Kidney FunctionSegmentation

Automatic Segmentation of the Kidneys and Cystic Renal Lesions on Non-Contrast CT Using a Convolutional Neural Network

2024-05-14 · Lucas Aronson, Ruben Ngnitewe Massaa, Syed Jamal Safdar Gardezi, Andrew L. Wentland

Objective: Automated segmentation tools are useful for calculating kidney volumes rapidly and accurately. Furthermore, these tools have the power to facilitate large-scale image-based artificial intelligence projects by …

Computed Tomography (CT)Deep LearningImage RegistrationSegmentation

Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge

2023-10-06 · Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu

Kidney and Kidney Tumor Segmentation Challenge (KiTS) 2023 offers a platform for researchers to compare their solutions to segmentation from 3D CT. In this work, we describe our submission to the challenge using automate…

SegmentationTumor Segmentation

Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests

2015-10-23 · Kanishka Sharma, Loic Peter, Christian Rupprecht, Anna Caroli 외

This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data. ADPKD severely alte…

Computed Tomography (CT)Segmentation

Automated Kidney Segmentation by Mask R-CNN in T2-weighted Magnetic Resonance Imaging

2021-08-27 · Manu Goyal, Junyu Guo, Lauren Hinojosa, Keith Hulsey 외

Despite the recent advances of deep learning algorithms in medical imaging, the automatic segmentation algorithms for kidneys in MRI exams are still scarce. Automated segmentation of kidneys in Magnetic Resonance Imaging…

Segmentation