How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models

TL;DR

This paper introduces a novel automated 3D segmentation method for prostate cancer metastases in PET images, leveraging 2D diffusion models on multi-angle projections to improve accuracy and robustness over existing 3D techniques.

Contribution

The study presents a new approach that uses 2D diffusion models on multi-angle projections for 3D lesion segmentation, enhancing performance in challenging cases.

Findings

Outperforms state-of-the-art 3D segmentation methods in accuracy.

Effective in detecting small metastatic lesions.

Demonstrates robustness across variable lesion sizes and locations.

Abstract

Prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging provides a tremendously exciting frontier in visualization of prostate cancer (PCa) metastatic lesions. However, accurate segmentation of metastatic lesions is challenging due to low signal-to-noise ratios and variable sizes, shapes, and locations of the lesions. This study proposes a novel approach for automated segmentation of metastatic lesions in PSMA PET/CT 3D volumetric images using 2D denoising diffusion probabilistic models (DDPMs). Instead of 2D trans-axial slices or 3D volumes, the proposed approach segments the lesions on generated multi-angle maximum intensity projections (MA-MIPs) of the PSMA PET images, then obtains the final 3D segmentation masks from 3D ordered subset expectation maximization (OSEM) reconstruction of 2D MA-MIPs segmentations. Our proposed method achieved superior performance compared to state-of-the-art 3D segmentation approaches in terms of accuracy and robustness in detecting and segmenting small metastatic PCa lesions. The proposed method has significant potential as a tool for quantitative analysis of metastatic burden in PCa patients.