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Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network.

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

Authors

Vivek Kumar Singh

Hatem A. Rashwan

Santiago Romani

Farhan Akram

Nidhi Pandey

Md. Mostafa Kamal Sarker

Adel Saleh

Meritxell Arenas

Miguel Arquez

Domenec Puig

Jordina Torrents-Barrena



Abstract

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast tumor within a region of interest (ROI) in a mammogram. The generative network learns to recognize the tumor area and to create the binary mask that outlines it. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. As a consequence, the proposed method outperforms several state-of-the-art approaches. Our working hypothesis is corroborated by diverse segmentation experiments performed on INbreast and a private in-house dataset. The proposed segmentation model, working on an image crop containing the tumor as well as a significant surrounding area of healthy tissue (loose frame ROI), provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four tumor shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on DDSM, since it provides shape ground truth (while the other two datasets does not), yielding an overall accuracy of 80%, which outperforms the current state-of-the-art.

Citation

SINGH, V.K., RASHWAN, H.A., ROMANI, S., AKRAM, F., PANDEY, N., SARKER, M.M.K., SALEH, A., ARENAS, M., ARQUEZ, M., PUIG, D. and TORRENTS-BARRENA, J. 2020. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert systems with applications [online], 139, article number 112855. Available from: https://doi.org/10.1016/j.eswa.2019.112855

Journal Article Type Article
Acceptance Date Jul 29, 2019
Online Publication Date Jul 29, 2019
Publication Date Jan 31, 2020
Deposit Date Dec 2, 2021
Publicly Available Date Mar 28, 2024
Journal Expert systems with applications
Print ISSN 0957-4174
Electronic ISSN 1873-6793
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 139
Article Number 112855
DOI https://doi.org/10.1016/j.eswa.2019.112855
Keywords Image recognition; Image classification; Artificial intelligence; Machine learning; Mammograms; Cancer
Public URL https://rgu-repository.worktribe.com/output/1538648

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