计算机科学
深度学习
人工智能
分割
前列腺癌
磁共振成像
水准点(测量)
边距(机器学习)
标杆管理
滑动窗口协议
模式识别(心理学)
卷积神经网络
放射科
癌症
计算机视觉
医学
机器学习
窗口(计算)
内科学
业务
操作系统
营销
地理
大地测量学
作者
Ruba Alkadi,Fatma Taher,Ayman El-Baz,Naoufel Werghi
标识
DOI:10.1007/s10278-018-0160-1
摘要
We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.
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