VNet-T2 algorithm dataset

This repository contains the scripts used to develop the VNet-T2 and the ASM model for prostate segmentation in MRI images:
- Salvi M., De Santi B., Pop B., Bosco M., V. Giannini, D. Regge, Molinari F., and Meiburger K. M. , "Integration of deep learning and active shape models for more accurate prostate segmentation in 3D MR images", Journal of Imaging 2022 (DOI: 10.3390/jimaging8050133)

Abstract: Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass the huge workload of physicians. In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2), followed by refinement using an Active Shape Model (ASM). While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours. Our method is developed and tested on a dataset composed of T2-weighted (T2w) MRI prostatic volumes of 60 male patients. In the test set, the proposed method shows excellent segmentation performance, achieving a mean dice score and Hausdorff distance of 0.851 and 7.55 mm, respectively. In the future, this algorithm could serve as an enabling technology for the development of computer-aided systems for prostate cancer characterization in MR imaging.

本仓库包含用于开发磁共振成像（Magnetic Resonance Imaging，MRI）图像前列腺分割任务的VNet-T2与主动形状模型（Active Shape Model，ASM）的脚本：
- Salvi M.、De Santi B.、Pop B.、Bosco M.、V. Giannini、D. Regge、Molinari F. 及Meiburger K. M. 于2022年发表于《Journal of Imaging》的论文《深度学习与主动形状模型的融合以实现3D MR图像中更精准的前列腺分割》（Integration of deep learning and active shape models for more accurate prostate segmentation in 3D MR images）（DOI: 10.3390/jimaging8050133）

摘要：磁共振成像（MRI）在前列腺癌的临床诊疗流程中应用日益广泛。然而，手动完成前列腺的三维（3D）分割是一项繁重且耗时的工作。在此背景下，采用自动化算法实现前列腺分割，可有效减轻临床医师的繁重工作负荷。本研究提出一种全自动的混合式分割方法，用于MR图像中的前列腺腺体分割：首先通过自定义的三维深度学习网络（VNet-T2）对前列腺容积进行初始分割，随后借助主动形状模型（ASM）对分割结果进行优化。深度学习网络聚焦于形状的三维空间一致性，而ASM则依赖局部图像信息，二者协同能够实现更精准的器官轮廓分割。本方法基于包含60名男性患者的T2加权（T2w）MRI前列腺容积数据集完成开发与测试。在测试集上，所提方法展现出优异的分割性能，平均戴斯系数（Dice score）与豪斯多夫距离（Hausdorff distance）分别达到0.851与7.55 mm。未来，该算法可作为一项赋能技术，用于开发MR成像场景下的前列腺癌计算机辅助表征系统。