Automatic segmentation of prostate cancer in 68Ga-prostate specific membrane antigen-11 PET/MRI based on diffusion models
10.13929/j.issn.1003-3289.2025.02.030
- VernacularTitle:基于扩散模型自动分割68Ga-前列腺特异性膜抗原-11 PET/MRI所示前列腺癌
- Author:
Wenwei HONG
1
;
Rushuai LI
;
Qingle MENG
;
Lei XU
Author Information
1. 浙江大学医学院附属第四医院医学工程科,浙江义乌 322000
- Publication Type:Journal Article
- Keywords:
prostatic neoplasms;
positron-emission tomography;
magnetic resonance imaging;
deep learning
- From:
Chinese Journal of Medical Imaging Technology
2025;41(2):326-330
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the effect of automatic segmentation of prostate cancer(PCa)in 68Ga-prostate specific membrane antigen(PSMA)-11 PET/MRI based on diffusion models.Methods A dataset contained 68Ga-PSMA-11 PET/MRI of 125 cases of PCa was preprocessed.Segmentation network was designed based on Faster-RCNN and spatial and channel reconstruction convolution(SCConv)Diffusion cascade,in which the first-level was used to coarsely localize the prostate and seminal vesicle glands using Faster-RCNN,and the second-level SCConv Diffusion network based on diffusion model was used to segment PCa.The effect of the above models for segmenting PCa in 68Ga-PSMA-11 PET/MRI were observed.Results The Dice similarity coefficient(DSC),intersection over union(IoU),and 95％Hausdorff distance(HD)of the Faster-RCNN+SCConv Diffusion model for segmenting PCa in 68Ga-PSMA-11 PET/MRI was 0.76,0.63 and 20.02 mm,all superior to those of nnU-Net(0.73,0.62 and 21.20 mm)and Faster-RCNN+nnU-Net(0.75,0.62 and 20.70 mm)models,and the segmentation for both single and multiple PCa were all accurate,with less missegment non-tumor tissue.Conclusion Diffusion model based on Faster-RCNN+SCConv diffusion cascade network could be used to completely and accurately segment PCa in 68Ga-PSMA-11 PET/MRI.
