基于金字塔机制融合网络的 T2 加权 MRI 前列腺及周围脂肪自动分割。

Automatic prostate and peri-prostatic fat segmentation based on pyramid mechanism fusion network for T2-weighted MRI.

机构信息

State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou 570288, China.

出版信息

Comput Methods Programs Biomed. 2022 Aug;223:106918. doi: 10.1016/j.cmpb.2022.106918. Epub 2022 Jun 20.

DOI:10.1016/j.cmpb.2022.106918

PMID:35779461

Abstract

BACKGROUND AND OBJECTIVE

Automatic and accurate segmentation of prostate and peri-prostatic fat in male pelvic MRI images is a critical step in the diagnosis and prognosis of prostate cancer. The boundary of prostate tissue is not clear, which makes the task of automatic segmentation very challenging. The main issues, especially for the peri-prostatic fat, which is being offered for the first time, are hazy boundaries and a large form variation.

METHODS

We propose a pyramid mechanism fusion network (PMF-Net) to learn global features and more comprehensive context information. In the proposed PMF-Net, we devised two pyramid techniques in particular. A pyramid mechanism module made of dilated convolutions of varying rates is inserted before each down sample of the fundamental network architecture encoder. The module is intended to address the issue of information loss during the feature coding process, particularly in the case of segmentation object boundary information. In the transition stage from encoder to decoder, pyramid fusion module is designed to extract global features. The features of the decoder not only integrate the features of the previous stage after up sampling and the output features of pyramid mechanism, but also include the features of skipping connection transmission under the same scale of the encoder.

RESULTS

The segmentation results of prostate and peri-prostatic fat on numerous diverse male pelvic MRI datasets show that our proposed PMF-Net has higher performance than existing methods. The average surface distance (ASD) and Dice similarity coefficient (DSC) of prostate segmentation results reached 10.06 and 90.21%, respectively. The ASD and DSC of the peri-prostatic fat segmentation results reached 50.96 and 82.41%.

CONCLUSIONS

The results of our segmentation are substantially connected and consistent with those of expert manual segmentation. Furthermore, peri-prostatic fat segmentation is a new issue, and good automatic segmentation has substantial therapeutic implications.

摘要

背景与目的

在男性盆腔 MRI 图像中自动且准确地分割前列腺和前列腺周围脂肪是诊断和预测前列腺癌的关键步骤。前列腺组织的边界不清晰，这使得自动分割任务极具挑战性。主要问题，特别是对于首次提供的前列腺周围脂肪，是边界模糊和形态变化较大。

方法

我们提出了一种金字塔机制融合网络（PMF-Net）来学习全局特征和更全面的上下文信息。在提出的 PMF-Net 中，我们特别设计了两种金字塔技术。在基本网络架构编码器的每个下采样之前插入一个由不同速率的扩张卷积组成的金字塔机制模块。该模块旨在解决特征编码过程中信息丢失的问题，特别是在分割对象边界信息的情况下。在从编码器到解码器的过渡阶段，设计了金字塔融合模块来提取全局特征。解码器的特征不仅在进行上采样后整合前一阶段的特征和金字塔机制的输出特征，还包括在编码器相同尺度下的跳过连接传输的特征。

结果

在许多不同的男性盆腔 MRI 数据集上对前列腺和前列腺周围脂肪的分割结果表明，我们提出的 PMF-Net 比现有方法具有更高的性能。前列腺分割结果的平均表面距离（ASD）和骰子相似系数（DSC）分别达到 10.06 和 90.21%。前列腺周围脂肪分割结果的 ASD 和 DSC 分别达到 50.96 和 82.41%。