Manual delineation of a rectal tumor on a volumetric image is time-consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2-weighted images, but automatic segmentation on diffusion-weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U-shaped neural network (U-Net) is proposed to automatically segment rectal tumors on diffusion-weighted images.

中文翻译：

---

通过 U-Net 深度学习在扩散加权图像上自动分割直肠肿瘤

在体积图像上手动描绘直肠肿瘤既费时又主观。深度学习已被用于在 T2 加权图像上自动分割直肠肿瘤，但扩散加权成像的自动分割受到噪声、伪影和低分辨率的挑战。在这项研究中，提出了一种体积 U 形神经网络 (U-Net) 来自动分割扩散加权图像上的直肠肿瘤。