Chilali O, Puech P, Lakroum S, Diaf M, Mordon S, Betrouni N
INSERM, U1189 - ONCO-THAI - Image Assisted Laser Therapy for Oncology, University of Lille, 59000, Lille, France.
Automatic Department, Mouloud Mammeri University, Tizi-Ouzou, Algeria.
J Digit Imaging. 2016 Dec;29(6):730-736. doi: 10.1007/s10278-016-9890-0.
For many years, prostate segmentation on MR images concerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts of the organ. In this paper, we propose an automatic segmentation method based on the use of T2W images and atlas images to segment the prostate and to isolate the peripheral and transition zones. The algorithm consists of two stages. First, the target image is registered with each zonal atlas image then the segmentation is obtained by the application of an evidential C-Means clustering. The method was evaluated on a representative and multi-centric image base and yielded mean Dice accuracy values of 0.81, 0.70, and 0.62 for the prostate, the transition zone, and peripheral zone, respectively.
多年来，磁共振图像上的前列腺分割仅涉及整个腺体的提取。目前，在聚焦治疗时代，对该器官不同部分进行分离的需求持续增长。在本文中，我们提出了一种基于使用T2加权图像和图谱图像的自动分割方法，用于分割前列腺并分离外周区和移行区。该算法由两个阶段组成。首先，将目标图像与每个区域图谱图像进行配准，然后通过应用证据C均值聚类获得分割结果。该方法在一个具有代表性的多中心图像库上进行了评估，前列腺、移行区和外周区的平均骰子准确度值分别为0.81、0.70和0.62。
