PDDS: A software for the early diagnosis of Parkinson’s disease from MRI and DaT scan images using detection and segmentation algorithms

Abstract

Parkinson’s disease is a common neurodegenerative disorder that affects millions of people worldwide. Medical imaging techniques such as MRI and DaTScan can help diagnose PD by showing the changes in the brain structures related to the disease, such as the substantia nigra and the striatum. In this paper, we propose a software called PDDS (Parkinson’s Disease Diagnosis Software) that can automatically detect and segment the deep brain regions from MRI and DaTScan images using advanced deep learning models. Our software uses YOLO and an ensemble of UNETs to accurately locate the regions of interest (ROIs) from T2-weighted MRI and DaTScan images respectively and use them for quantitative assessment and decision support. The ensemble approach uses a weighted average ensemble which achieves the highest mean IOU after a grid search technique was implemented to get the appropriate weights. We used mosaic data augmentation to improve the generalization ability of our models by exposing them to objects in different contexts. The proposed architecture was trained and validated using the PPMI and IXI datasets and the detection results show that the YOLOv7x model achieves a mAP_0.5:0.95 of 70.39 % for DaTScan images and a mAP_0.5:0.95 of 64.16 % for MRI images, outperforming previous methods. For the segmentation results, our ensemble UNet model with different backbones achieves a mean IOU of 70.02 % for DaTScan images and 54.31 % for MRI images. According to our findings, our approach is a competitive automatic detection and segmentation method and can be applied to clinically challenging medical imaging problems.