Volumetric images from Magnetic Resonance Imaging (MRI) provide invaluable information in preoperative staging of rectal cancer. Above all, accurate preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment, as chemo-radiotherapy is usually recommended to patients with T3 (or greater) stage cancer. In this study, we present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes. Specifically, we propose 1) a custom ResNet-based volume encoder that models the inter-slice relationship with late fusion (i.e., 3D convolution at the last layer), 2) a bilinear computation that aggregates the resulting features from the encoder to create a volume-wise feature, and 3) a joint minimization of triplet loss and focal loss. With MR volumes of pathologically confirmed T2/T3 rectal cancer, we perform extensive experiments to compare various designs within the framework of residual learning. As a result, our network achieves an AUC of 0.831, which is higher than the reported accuracy of the professional radiologist groups. We believe this method can be extended to other volume analysis tasks
翻译:磁共振成像(MRI)的体积图像为直肠癌的预发发发作提供了宝贵的信息。 最重要的是,T2和T3阶段之间准确的预发性歧视可以说是直肠癌治疗中最具挑战性和临床意义的任务,因为通常建议T3(或更大)级癌症患者接受化学放射治疗。在本研究中,我们提出了一个量子共变神经网络,以精确区分T2和T3级直肠癌的直肠体积体积。具体地说,我们建议1个基于定制ResNet的体积编码器,以模拟与晚聚(即最后一层的3D演进)之间的切发性关系,2个双线计算法,将诱变的特征综合起来,以创造量性特征;3个共同尽量减少三重损失和焦线损失。在病理学上证实的T2/T3直肠癌体积中,我们进行了广泛的实验,以比较残余学习框架内的各种设计。结果是,我们的网络能够实现AUCLEAR3的精确度,而我们所报告的专业分析是更高。