Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Dades identificatives

    Identificador: imarina:5808108
    Autors:
    Kumar Singh, VivekRashwan, Hatem ARomani, SantiagoAkram, FarhanPandey, NidhiKamal Sarker, Md MostafaSaleh, AdelArenas, MeritxellArquez, MiguelPuig, DomenecTorrents-Barrena, Jordina
    Resum:
    © 2019 Elsevier Ltd 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.
  • Altres:

    Autor segons l'article: Kumar Singh, Vivek; Rashwan, Hatem A; Romani, Santiago; Akram, Farhan; Pandey, Nidhi; Kamal Sarker, Md Mostafa; Saleh, Adel; Arenas, Meritxell; Arquez, Miguel; Puig, Domenec; Torrents-Barrena, Jordina
    Departament: Ciències Mèdiques Bàsiques Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / AKRAM, FARHAN / Arenas Prat, Meritxell / Pandey, Nidhi / Puig Valls, Domènec Savi / Romaní Also, Santiago
    Paraules clau: Tumor segmentation and shape classification Mass Mammograms Convolutional neural network Conditional generative adversarial network Computer-aided detection
    Resum: © 2019 Elsevier Ltd 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.
    Àrees temàtiques: Química Operations research & management science Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General engineering Farmacia Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação Economia Direito Computer science, artificial intelligence Computer science applications Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Artificial intelligence Arquitetura, urbanismo e design Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 09574174
    Adreça de correu electrònic de l'autor: hatem.abdellatif@urv.cat meritxell.arenas@urv.cat santiago.romani@urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0001-5421-1637 0000-0003-0815-2570 0000-0001-6673-9615 0000-0002-0562-4205
    Data d'alta del registre: 2024-09-21
    Versió de l'article dipositat: info:eu-repo/semantics/submittedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0957417419305573?via%3Dihub
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Expert Systems With Applications. 139 (UNSP 112855): 112855-
    Referència de l'ítem segons les normes APA: Kumar Singh, Vivek; Rashwan, Hatem A; Romani, Santiago; Akram, Farhan; Pandey, Nidhi; Kamal Sarker, Md Mostafa; Saleh, Adel; Arenas, Meritxell; Arquez (2020). Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Systems With Applications, 139(UNSP 112855), 112855-. DOI: 10.1016/j.eswa.2019.112855
    DOI de l'article: 10.1016/j.eswa.2019.112855
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Artificial Intelligence,Computer Science Applications,Computer Science, Artificial Intelligence,Engineering (Miscellaneous),Engineering, Electrical & Electronic,Operations Research & Management Science
    Tumor segmentation and shape classification
    Mass
    Mammograms
    Convolutional neural network
    Conditional generative adversarial network
    Computer-aided detection
    Química
    Operations research & management science
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    General engineering
    Farmacia
    Engineering, electrical & electronic
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Enfermagem
    Educação
    Economia
    Direito
    Computer science, artificial intelligence
    Computer science applications
    Ciências sociais aplicadas i
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
    Artificial intelligence
    Arquitetura, urbanismo e design
    Administração, ciências contábeis e turismo
    Administração pública e de empresas, ciências contábeis e turismo
  • Documents:

  • Cerca a google

    Search to google scholar