IEEE/ACM Trans Comput Biol Bioinform. 2019 Nov-Dec;16(6):1794-1801. doi: 10.1109/TCBB.2018.2835444. Epub 2018 May 11.
The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e., the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91 percent specificity and 90 percent average accuracy over the targeted CEUS images for prostate cancer detection, which was superior ( ) than previously reported approaches and implementations.
血管生成在癌症发展中的重要作用促使许多研究人员基于超声造影（CEUS）成像技术来探索非侵入性癌症诊断的前景。本文提出了一种深度学习框架，用于在连续的 CEUS 图像中检测前列腺癌。所提出的方法通过执行三维卷积操作，从空间和时间两个维度均匀地提取特征，从而捕获多个相邻帧中编码的灌注过程的动态信息，用于前列腺癌检测。深度学习模型在使用两种类型的造影剂（即针对前列腺癌细胞的抗 PSMA 靶向剂和非靶向空白剂）记录的 CEUS 图像上，针对专家勾画进行了训练和验证。实验表明，该深度学习方法在针对前列腺癌的靶向 CEUS 图像上的检测中，特异性超过 91%，平均准确率超过 90%，优于（ ）先前报道的方法和实现。
