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Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.
Han Jiao1, Xinhua Jiang2, Zhiyong Pang1
1School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China.
Computational and Mathematical Methods in Medicine
|May 27, 2020
Summary
Deep learning models automate breast cancer analysis by accurately segmenting breasts and detecting masses in MRI scans. This approach enhances diagnostic efficiency and reduces radiologist workload.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Accurate breast cancer diagnosis and monitoring rely on precise segmentation and mass detection in medical images.
- Manual analysis of breast cancer imaging is time-consuming and prone to high workloads for radiologists.
- Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a key modality for breast cancer assessment.
Purpose of the Study:
- To develop and evaluate deep learning models for automated breast segmentation and mass detection in DCE-MRI.
- To improve the efficiency and accuracy of breast cancer analysis in radiological workflows.
- To compare the performance of advanced deep learning architectures against baseline methods.
Main Methods:
- Utilized a U-Net++ architecture for fully convolutional neural network-based breast segmentation.
- Employed a Faster Region-based Convolutional Neural Network (Faster R-CNN) for mass detection on segmented breast images.
- Validated the models on a dataset of DCE-MRI from 75 patients using 5-fold cross-validation.
Main Results:
- Achieved high performance in breast region segmentation with Dice Similarity Coefficient (DSC) of 0.951, Jaccard coefficient of 0.908, and segmentation sensitivity of 0.948.
- Demonstrated superior segmentation performance compared to the original U-Net algorithm.
- Attained an average sensitivity of 0.874 for mass detection with 3.4 false positives per case.
Conclusions:
- Deep convolutional neural networks (DCNNs) effectively automate breast segmentation and mass detection in DCE-MRI.
- The proposed deep learning approach significantly enhances the accuracy and efficiency of breast cancer analysis.
- These automated methods show promise in assisting radiologists and reducing manual workload in clinical practice.
