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

• URL: http://arxiv.org/abs/2310.04110v1
• Date: Fri, 6 Oct 2023 09:20:22 GMT
• Title: Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge
• Authors: Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu
• Abstract summary: We describe our submission to the Kidney and Kidney Tumor Challenge (KiTS) 2023. Our solution achieves the average dice of 0.835 and surface dice of 0.723, which ranks first and wins the KiTS 2023 challenge.
• Score: 11.820386742605539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 automated segmentation of Auto3DSeg available in MONAI. Our solution achieves the average dice of 0.835 and surface dice of 0.723, which ranks first and wins the KiTS 2023 challenge.

Related papers

• Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks [62.443665295250035]
  We present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023) In total, 32 competing teams register for the challenge, from which we received 11 successful submissions.
  arXiv  Detail & Related papers  (2024-07-20T10:13:54Z)
• The Third Monocular Depth Estimation Challenge [134.16634233789776]
  This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC) The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.
  arXiv  Detail & Related papers  (2024-04-25T17:59:59Z)
• Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge [16.189621599350684]
  In 2023, it is estimated that 81,800 kidney cancer cases will be newly diagnosed, and 14,890 people will die from this cancer in the United States. There exists inter-observer variability due to subtle differences in the imaging features of kidney and kidney tumors.
  arXiv  Detail & Related papers  (2023-12-09T10:42:50Z)
• The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT [50.41526598153698]
  This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Challenge (KiTS21) KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed. 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.
  arXiv  Detail & Related papers  (2023-07-05T02:00:14Z)
• The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data [60.94672667514737]
  This study implements the Type Three (T3) challenge format, which allows for training solutions on private data. With T3, challenge organizers train a provided by the participants on sequestered training data. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815.
  arXiv  Detail & Related papers  (2023-06-18T05:48:28Z)
• Automated head and neck tumor segmentation from 3D PET/CT [9.814838162752112]
  Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform to compare solutions to segmentation of tumors and lymph nodes from 3D CT and PET images. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. Our solution achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802.
  arXiv  Detail & Related papers  (2022-09-22T06:24:09Z)
• Automated segmentation of intracranial hemorrhages from 3D CT [9.814838162752114]
  Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs. We use a 2D segmentation network, SegResNet from MONAI, operating slice-wise without resampling.
  arXiv  Detail & Related papers  (2022-09-21T20:37:32Z)
• Automated ischemic stroke lesion segmentation from 3D MRI [8.52488593202588]
  Ischemic Stroke Lesion challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs. We re-sample all images to a common resolution, use two input MRI modalities (DWI and ADC) and train SegResNet semantic segmentation network from MONAI.
  arXiv  Detail & Related papers  (2022-09-20T08:21:57Z)
• Optimized U-Net for Brain Tumor Segmentation [62.997667081978825]
  We propose an optimized U-Net architecture for a brain mboxtumor segmentation task in the BraTS21 Challenge. Our solution was the winner of the challenge validation phase, with the normalized statistical ranking score of 0.267 and mean Dice score of 0.8855.
  arXiv  Detail & Related papers  (2021-10-07T11:44:09Z)
• SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results [90.42321856720633]
  SHARP 2020 is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction and the amount of completed data.
  arXiv  Detail & Related papers  (2020-10-26T12:05:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.