Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

By Xiahai Zhuang, Christian Payer, Darko Štern, Martin Urschler, Mattias Paul Heinrich, J. Oster, Chunliang Wang, Ö. Smedby, C. Bian, Xin Yang and 23 more. Medical Image Anal., 2019

346 citations recorded by the source corpus.

Highlights • This work presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.• This work introduces the related information to the challenge, discusses the results from the conventional methods and deep learning-based algorithms, and provides insights to the future research.• The challenge provides a fair and intuitive comparison framework for methods developed and being developed for WHS.• The challenge provides the training datasets with manually delineated ground truths and evaluation for an ongoing development of MM-WHS algorithms.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f37d035acaa36fccb7bf30569c4af926697b79bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

Daniel Rueckert

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Med3D: Transfer Learning for 3D Medical Image Analysis (2019, background): “Over the past decade, over 100 challenges [14] have been organized within the area of biomedical image analysis [15, 16, 17, 18, 19].”
Unpaired Multi-Modal Segmentation via Knowledge Distillation (2020, methodology): “mentation using the MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge [36] dataset, which consists of”
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2021, neutral): “With this purpose, we employ two new datasets: MMWHS [76], and LASC13 [62].”
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation (2019, methodology): “The authors use the challenge dataset by Zhuang et al. (2019), which contains 20”
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge (2019, background): “Despite these challenges, recent work in this area has demonstrated impressive performance on the 3D segmentation of a wide variety of anatomical structures and lesions in cross-sectional imaging (Maier et al., 2017; Bakas et al., 2018; Bilic et al., 2019; Zhuang et al., 2019).”
Deep Learning for Cardiac Image Segmentation: A Review (2019, methodology): “These methods generally achieve better segmentation accuracy on CT images compared to that of MR images, mainly because of the smaller variations in image intensity distribution across different CT scanners and better image quality (19).”