Deep generative model-driven multimodal prostate segmentation in radiotherapy

TL;DR

This paper introduces a deep generative model-driven approach for multimodal prostate segmentation in radiotherapy, leveraging shape modeling and registration to improve accuracy across MRI and CT images.

Contribution

The novel DGMNet method embeds generative neural networks for shape modeling and adapts to different modalities via learning-based registration, enhancing segmentation accuracy.

Findings

Effective across MRI and CT datasets from multiple centers

Accurate prostate segmentation in low contrast images

Outperforms existing methods in robustness and accuracy

Abstract

Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet). The novelty of our method comes with its embedded generative neural network for learning-based shape modeling and its ability to adapt for different imaging modalities via learning-based registration. The proposed method includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural networkbased shape modeling trained on a reliable contrast imaging modality (such as MRI) can be directly applied to low contrast imaging modality (such as CT) to achieve accurate prostate segmentation. The method was evaluated on MRI and CT datasets acquired from different clinical centers with large variations in contrast and scanning protocols. Experimental results reveal that our method can be used to automatically and accurately segment the prostate gland in different imaging modalities.