Next Article in Journal
IVAN: An Interactive Herlofson’s Nomogram Visualizer for Local Weather Forecast
Previous Article in Journal
A Hybrid Scheme for an Interoperable Identity Federation System Based on Attribute Aggregation Method
Previous Article in Special Issue
Cloud-Based Image Retrieval Using GPU Platforms
Article Menu

Export Article

Open AccessArticle

MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Computer Science Unit, Faculty of Engineering, University of Mons, Place du Parc, 20, 7000 Mons, Belgium
*
Author to whom correspondence should be addressed.
Computers 2019, 8(3), 52; https://doi.org/10.3390/computers8030052
Received: 18 June 2019 / Revised: 27 June 2019 / Accepted: 28 June 2019 / Published: 29 June 2019
  |  
PDF [2974 KB, uploaded 5 July 2019]
  |  

Abstract

Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture. View Full-Text
Keywords: breast tumor segmentation; MRI; encoder–decoder; deep learning; HPC; SegNet; U-Net breast tumor segmentation; MRI; encoder–decoder; deep learning; HPC; SegNet; U-Net
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

El Adoui, M.; Mahmoudi, S.A.; Larhmam, M.A.; Benjelloun, M. MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures. Computers 2019, 8, 52.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top