Ezra AI Canada, Unit 310, 545 King St. West, Toronto, Canada.
Université de Rennes 1, Rennes, France.
Int J Comput Assist Radiol Surg. 2019 Oct;14(10):1647-1650. doi: 10.1007/s11548-019-01967-5. Epub 2019 Apr 10.
To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI).
A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study.
The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations.
This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.
在多参数磁共振图像（mp-MRI）上自动识别疑似前列腺癌的区域。
在 T2 加权像、表观扩散系数图和高 b 值扩散加权图像的专家放射科医生的分割基础上，实现了一个残差网络。本研究使用了 346 名患者的 mp-MRI。
该残差网络在病灶检测方面的命中或错失准确率达到 93%，网络与放射科医生分割之间的平均 Jaccard 评分达到 71%，表明两者的一致性较好。
本文证明了残差网络能够学习前列腺病变分割的特征。
