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Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification.

Singh, Vivek Kumar; Romani, Santiago; Rashwan, Hatem A.; Akram, Farhan; Pandey, Nidhi; Sarker, Md. Mostafa Kamal; Abdulwahab, Saddam; Torrents-Barrena, Jordina; Saleh, Adel; Arquez, Miguel; Arenas, Meritxell; Puig, Domenec

Authors

Vivek Kumar Singh

Santiago Romani

Hatem A. Rashwan

Farhan Akram

Nidhi Pandey

Saddam Abdulwahab

Jordina Torrents-Barrena

Adel Saleh

Miguel Arquez

Meritxell Arenas

Domenec Puig



Abstract

This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the public DDSM dataset and from our in-house private dataset confirm our hypothesis with very high Dice coefficient and Jaccard index (>94% and >89%, respectively) outperforming the scores obtained by other state-of-the-art approaches. Furthermore, in order to detect portray significant morphological features of the segmented tumor, a specific Convolutional Neural Network (CNN) have also been designed for classifying the segmented tumor areas into four types (irregular, lobular, oval and round), which provides an overall accuracy about 72% with the DDSM dataset.

Citation

SINGH, V.K., ROMANI, S., RASHWAN, H.A., AKRAM, F., PANDEY, N., SARKER, M.M.K., ABDULWAHAB, S., TORRENTS-BARRENA, J., SALEH, A., ARQUEZ, M., ARENAS, M. and PUIG, D. 2018. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 833-840. Available from: https://doi.org/10.1007/978-3-030-00934-2_92

Conference Name 1st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018)
Conference Location Granada, Spain
Start Date Sep 16, 2018
End Date Sep 20, 2018
Acceptance Date Mar 2, 2018
Online Publication Date Sep 26, 2018
Publication Date Dec 31, 2018
Deposit Date Dec 4, 2021
Publicly Available Date Jan 18, 2022
Publisher Springer
Pages 833-840
Series Title Lecture notes in computer science (LNCS)
Series Number 11071
Series ISSN 0302-9743
Book Title Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain
ISBN 9783030009335
DOI https://doi.org/10.1007/978-3-030-00934-2_92
Keywords cGAN; CNN; Mammography; Mass segmentation; Mass shape classification
Public URL https://rgu-repository.worktribe.com/output/1542095

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Copyright Statement
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-00934-2_92. Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.





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