Prostate lesion detection in an axial T2 weighted (T2W) MR images is a very challenging task due to heterogeneous and inconsistent pixel representation surrounding the prostate boundary. In this paper, a radiomics based deeply supervised U-Net is proposed for both prostate gland and prostate lesion segmentation. The proposed pipeline is trained and validated on 1174 and 2071 T2W MR images of 40 patients and tested on 250 and 415 T2W MR images of 10 patients for prostate capsule segmentation and prostate lesion segmentation, respectively. Effective segmentation of prostate lesions in various stages of prostate cancer (namely T1, T2, T3, and T4) is achieved using the proposed framework. The mean Dice Similarity Coefficient (DSC) for actual prostate capsule segmentation and prostate lesion segmentation is 0.8958 and 0.9176, respectively. The proposed framework is also tested on Promise12 public dataset for performance analysis in segmenting prostate gland. The segmentation results using proposed architecture are promising compared to state-of-the-art techniques. It also improves the accuracy of the prostate cancer diagnosis.

由于围绕前列腺边界的异质性和不一致的像素表示，轴向T2加权（T2W）MR图像中的前列腺病变检测是一项非常具有挑战性的任务。在本文中，提出了一种基于放射学的深监督U-Net，用于前列腺和前列腺病变的分割。对40名患者的1174和2071 T2W MR图像进行了培训和验证，并对10名患者的250和415 T2W MR图像分别进行了前列腺囊膜分割和前列腺病变分割的测试和验证。使用提出的框架可以实现前列腺癌各个阶段（即T1，T2，T3和T4）中前列腺病变的有效分割。实际前列腺包膜分割和前列腺病变分割的平均骰子相似系数（DSC）分别为0.8958和0.9176。提议的框架也已在Promise12公共数据集上进行了测试，以分析前列腺分割性能。与最新技术相比，使用建议的体系结构进行分割的结果很有希望。它还提高了前列腺癌诊断的准确性。