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A large annotated medical image dataset for the development and evaluation of segmentation algorithms

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A large annotated medical image dataset for the development and evaluation of segmentation algorithms. / Simpson, Amber L.; Antonelli, Michela; Bakas, Spyridon et al.

2019. ( arXiv).

Research output: Working paper/PreprintPreprint

Harvard

Simpson, AL, Antonelli, M, Bakas, S, Bilello, M, Farahani, K, Ginneken, BV, Kopp-Schneider, A, Landman, BA, Litjens, G, Menze, B, Ronneberger, O, Summers, RM, Bilic, P, Christ, PF, Do, RKG, Gollub, M, Golia-Pernicka, J, Heckers, SH, Jarnagin, WR, McHugo, MK, Napel, S, Vorontsov, E, Maier-Hein, L & Cardoso, MJ 2019 'A large annotated medical image dataset for the development and evaluation of segmentation algorithms' arXiv.

APA

Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Ginneken, B. V., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., Bilic, P., Christ, P. F., Do, R. K. G., Gollub, M., Golia-Pernicka, J., Heckers, S. H., Jarnagin, W. R., ... Cardoso, M. J. (2019). A large annotated medical image dataset for the development and evaluation of segmentation algorithms. ( arXiv).

Vancouver

Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, Ginneken BV et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. 2019 Feb 25. ( arXiv).

Author

Simpson, Amber L. ; Antonelli, Michela ; Bakas, Spyridon et al. / A large annotated medical image dataset for the development and evaluation of segmentation algorithms. 2019. ( arXiv).

Bibtex Download

@techreport{abe938e1ca7e4165bbf7e76ecdf0d6cf,
title = "A large annotated medical image dataset for the development and evaluation of segmentation algorithms",
abstract = " Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. ",
keywords = "cs.CV, eess.IV",
author = "Simpson, {Amber L.} and Michela Antonelli and Spyridon Bakas and Michel Bilello and Keyvan Farahani and Ginneken, {Bram van} and Annette Kopp-Schneider and Landman, {Bennett A.} and Geert Litjens and Bjoern Menze and Olaf Ronneberger and Summers, {Ronald M.} and Patrick Bilic and Christ, {Patrick F.} and Do, {Richard K. G.} and Marc Gollub and Jennifer Golia-Pernicka and Heckers, {Stephan H.} and Jarnagin, {William R.} and McHugo, {Maureen K.} and Sandy Napel and Eugene Vorontsov and Lena Maier-Hein and Cardoso, {M. Jorge}",
year = "2019",
month = feb,
day = "25",
language = "English",
series = " arXiv",
type = "WorkingPaper",

}

RIS (suitable for import to EndNote) Download

TY - UNPB

T1 - A large annotated medical image dataset for the development and evaluation of segmentation algorithms

AU - Simpson, Amber L.

AU - Antonelli, Michela

AU - Bakas, Spyridon

AU - Bilello, Michel

AU - Farahani, Keyvan

AU - Ginneken, Bram van

AU - Kopp-Schneider, Annette

AU - Landman, Bennett A.

AU - Litjens, Geert

AU - Menze, Bjoern

AU - Ronneberger, Olaf

AU - Summers, Ronald M.

AU - Bilic, Patrick

AU - Christ, Patrick F.

AU - Do, Richard K. G.

AU - Gollub, Marc

AU - Golia-Pernicka, Jennifer

AU - Heckers, Stephan H.

AU - Jarnagin, William R.

AU - McHugo, Maureen K.

AU - Napel, Sandy

AU - Vorontsov, Eugene

AU - Maier-Hein, Lena

AU - Cardoso, M. Jorge

PY - 2019/2/25

Y1 - 2019/2/25

N2 - Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.

AB - Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.

KW - cs.CV

KW - eess.IV

M3 - Preprint

T3 - arXiv

BT - A large annotated medical image dataset for the development and evaluation of segmentation algorithms

ER -

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