The Medical Segmentation Decathlon

Antonelli, Michela; Reinke, Annika; Bakas, Spyridon; Farahani, Keyvan; Kopp‐Schneider, Annette; Landman, Bennett A.; Litjens, Geert; Menze, Bjoern; Ronneberger, Olaf; Summers, Ronald M.; Ginneken, Bram van; Bilello, Michel; Bilic, Patrick; Christ, Patrick Ferdinand; Richard, K. G.; Gollub, Marc J.; Heckers, Stephan; Huisman, Henkjan; Jarnagin, William R.; McHugo, Maureen; Napel, Sandy; Pernicka, Jennifer S. Goli; Rhode, Kawal; Tobon-Gómez, Catalina; Vorontsov, Eugene; Meakin, James; Ourselin, Sébastien; Wiesenfarth, Manuel; Arbeláez, Pablo; Bae, Byeonguk; Chen, Sihong; Daza, Laura; Feng, Jianjiang; He, Baochun; Isensee, Fabian; Ji, Yuanfeng; Jia, Fucang; Kim, Namkug; Kim, Ildoo; Merhof, Dorit; Pai, Akshay; Park, Beomhee; Perslev, Mathias; Rezaiifar, R.; Rippel, Oliver; Sarasúa, Ignacio; Shen, Wei; Son, Jaemin; Wachinger, Christian; Wang, Liansheng; Wang, Yan; Xia, Yingda; Xu, Daguang; Xu, Zhanwei; Zheng, Yefeng; Simpson, Amber L.; Maier-Hein, Lena; Cardoso, M. Jorge

doi:10.1038/s41467-022-30695-9

Cited by 540 publications

(174 citation statements)

References 35 publications

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“…To verify the effectiveness of the proposed method, BraTS2018 (Menze et al, 2014 ; Bakas et al, 2017 , 2018 ) and the cardiac segmentation dataset in the medical segmentation (Antonelli et al, 2022 ) decathlon are used as training and testing datasets in the experiments. The BraTS2018 dataset has 285 annotated brain tumor magnetic resonance imaging (MRI) cases, and each case has four different modalities, namely Flair, T1, T1ce, and T2.…”

Section: Methodsmentioning

confidence: 99%

A medical image segmentation method based on multi-dimensional statistical features

et al. 2022

Front. Neurosci.

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Medical image segmentation has important auxiliary significance for clinical diagnosis and treatment. Most of existing medical image segmentation solutions adopt convolutional neural networks (CNNs). Althought these existing solutions can achieve good image segmentation performance, CNNs focus on local information and ignore global image information. Since Transformer can encode the whole image, it has good global modeling ability and is effective for the extraction of global information. Therefore, this paper proposes a hybrid feature extraction network, into which CNNs and Transformer are integrated to utilize their advantages in feature extraction. To enhance low-dimensional texture features, this paper also proposes a multi-dimensional statistical feature extraction module to fully fuse the features extracted by CNNs and Transformer and enhance the segmentation performance of medical images. The experimental results confirm that the proposed method achieves better results in brain tumor segmentation and ventricle segmentation than state-of-the-art solutions.

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Section: Methodsmentioning

confidence: 99%

A medical image segmentation method based on multi-dimensional statistical features

et al. 2022

Front. Neurosci.

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“…A frequently applied strategy to improve model performance is to remove segments below a fixed volume threshold. 31 For example, a reasonable volume threshold can be based on an assumed detection limit for VS in routine clinical MRI of 2mm, which corresponds to a cubic volume of 8mm 3 . In comparison, the smallest predicted volume on the MC-RC dataset was 30mm 3 .…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI

Kujawa

Dorent

Connor

et al. 2022

Preprint

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ObjectiveAutomatic segmentation of vestibular schwannoma (VS) from routine clinical MRI can improve clinical workflow, facilitate treatment decisions, and assist patient management. Previously, excellent automatic segmentation results were achieved on datasets of standardised MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms. Here, we show that automatic segmentation of VS on such datasets is also possible with high accuracy.MethodsWe acquired a large multi-centre routine clinical (MC-RC) dataset of 168 patients with a single sporadic VS who were referred from 10 medical sites and consecutively seen at a single centre. Up to three longitudinal MRI exams were selected for each patient. Selection rules based on image modality, resolution orientation, and acquisition timepoint were defined to automatically select contrast-enhanced T1-weighted (ceT1w) images (n=130) and T2-weighted images (n=379). Manual ground truth segmentations were obtained in an iterative process in which segmentations were: 1) produced or amended by a specialized company; and 2) reviewed by one of three trained radiologists; and 3) validated by an expert team. Inter- and intra-observer reliability was assessed on a subset of 10 ceT1w and 41 T2w images. The MC-RC dataset was split randomly into 3 nonoverlapping sets for model training, hyperparameter-tuning and testing in proportions 70/10/20%. We applied deep learning to train our VS segmentation model, based on convolutional neural networks (CNN) within the nnU-Net framework.ResultsOur model achieved excellent Dice scores when evaluated on the MC-RC testing set as well as the public testing set. On the MC-RC testing set, Dice scores were 90.8±4.5% for ceT1w, 86.1±11.6% for T2w and 82.3±18.4% for a combined ceT1w+T2w input.ConclusionsWe developed a model for automatic VS segmentation on diverse multi-centre clinical datasets. The results show that the performance of the framework is comparable to that of human annotators. In contrast, a model trained a publicly available dataset acquired for Gamma Knife stereotactic radiosurgery did not perform well on the MC-RC testing set. The application of our model has the potential to greatly facilitate the management of patients in clinical practice. Our pre-trained segmentation models are made available online. Moreover, we are in the process of making the MC-RC dataset publicly available.

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“…It allows the semantic segmentation tasks to be approached with standardized pipelines [ 15 , 38 ], and its architecture is based on those of U-Net and U-Net 3D. It was originally conceived during the Medical Decathlon Segmentation Challenge [ 39 ], where it emerged as the leading approach in all tasks. Advantages of this method consist of automatic configurations of preprocessing, data augmentation, training, inference, and postprocessing.…”

Section: Methodsmentioning

confidence: 99%

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

et al. 2022

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In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

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The Medical Segmentation Decathlon

Cited by 540 publications

References 35 publications

A medical image segmentation method based on multi-dimensional statistical features

A medical image segmentation method based on multi-dimensional statistical features

Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

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