Summary
- A new model named MM-UNet was applied to assist in prostate MR image segmentation. This was performed on two datasets with professional segmentation masks.
- Performance metrics like DSC, 95HD and ASD were enlisted for segmentation accuracy.
- MM-UNet achieved the best mean segmentation performance compared to nine state-of-the-art methods on two public datasets.
- Ablation studies revealed how the different components helped segmentation, and MM-UNet made statistically significant improvements over baseline in performance.
Prostate cancer is a wide spread condition among males across the globe. Imaging techniques such as MRI can be used by medical professionals to diagnose and follow the progress of the disease. A study tested a new model, dubbed MM-UNet to increase the precision of prostate MR image segmentation (as segmenting frame is manually drawn by radiologists).
Two different datasets, PROMISE12 and ASPS13 were used for checking the performance of MM-UNet. Results showed that MM-Unet outperforms other state-of-the-art segmentation methods for all metrics. These provide metrics regarding how well the segmentation results match up to actual prostate boundaries in the images.
As the study shows, MM-UNet effectively segmented prostate MR images. The model can give more accurate and dependable results than traditional method thanks to applying new methods and algorithms. The findings may improve the diagnosis and treatment planning of prostate cancer patients.
Overall, the MM-UNet model has prospects to play significant role in enhancing prostate MR image segmentation – an essential stage during diagnostic and therapy of prostate cancer. Groundbreaking new technology and methods are used to enable advances in medical imaging accuracy, while also improving speed. Future research will further investigate the efficacy of models similar to MM-UNet in improving clinical outcomes and health care services for prostate cancer patients.
Radiology,Urology