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

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

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 1st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018)
• 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
• Peer Reviewed: Peer Reviewed
• 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