VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

• URL: http://arxiv.org/abs/2001.09193v6
• Date: Tue, 5 Apr 2022 08:17:55 GMT
• Title: VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
• Authors: Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat, Maximilian L\"offler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kuka\v{c}ka, Christian Payer, Darko \v{S}tern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicol\'as P\'erez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Kevin Brown, Alexandre Kirszenberg, \'Elodie Puybareau, Di Chen, Yiwei Bai, Brandon H. Rapazzo, Timyoas Yeah, Amber Zhang, Shangliang Xu, Feng Hou, Zhiqiang He, Chan Zeng, Zheng Xiangshang, Xu Liming, Tucker J. Netherton, Raymond P. Mumme, Laurence E. Court, Zixun Huang, Chenhang He, Li-Wen Wang, Sai Ho Ling, L\^e Duy Hu\`ynh, Nicolas Boutry, Roman Jakubicek, Jiri Chmelik, Supriti Mulay, Mohanasankar Sivaprakasam, Johannes C. Paetzold, Suprosanna Shit, Ivan Ezhov, Benedikt Wiestler, Ben Glocker, Alexander Valentinitsch, Markus Rempfler, Bj\"orn H. Menze and Jan S. Kirschke
• Abstract summary: Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
• Score: 121.31355003451152
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and population-based analysis on spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms towards labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The content and code concerning VerSe can be accessed at: https://github.com/anjany/verse.

Related papers

• A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge [30.611482996378683]
  Image and disease variability hinder the development of generalizable AI algorithms with clinical value. We present a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion (ISLES) challenge. We combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions.
  arXiv  Detail & Related papers  (2024-03-28T13:56:26Z)
• Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge [66.86170104167608]
  The RibFrac Challenge provides a benchmark dataset of over 5,000 rib fractures from 660 CT scans. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts.
  arXiv  Detail & Related papers  (2024-02-14T18:18:33Z)
• SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation [12.427024671144869]
  Vertebrae identification in arbitrary fields-of-view plays a crucial role in diagnosing spine disease. Existing methods at the spine-level are unable to meet this challenge. We propose a three-stage method to address the challenges in 3D CT vertebrae identification at vertebrae-level.
  arXiv  Detail & Related papers  (2024-01-14T12:02:39Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Lumbar spine segmentation in MR images: a dataset and a public benchmark [2.768537261519943]
  This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain.
  arXiv  Detail & Related papers  (2023-06-21T12:19:17Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data [0.0]
  We report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru) VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae.
  arXiv  Detail & Related papers  (2021-03-10T22:07:26Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.