For cardiologists, inspecting the vascular structures is of very high…
Read more about "Aortic segmentation of 4D flow PCMRA images using deep learning techniques"
In this project, we are working on developing a fully…
Read more about "End to end analysis of non-small cell lung cancer using Low Dose CT images"
The limited number of manually labelled data is the biggest challenge in implementation supervised training algorithms in medical image processing. Therefore, we have implemented the transfer learning approach to improve fully automated kidney segmentation on small multi-parametric (mp-MRI) dataset sequences using Attention U-Net deep learning model. Due to the difference in acquisition protocols in mp-MRI sequences a single model will not generalize on all mp-MRI sequence images, but the knowledge gained by model during T1-Weighted contrast enhanced Nephrographic Phase (T1W-NG) training when transferred to small size target dataset (T2W, T1-CM, T1-PRE, T1-IP, and T1-OOP) improved the kidney segmentation results.
In this study a total of 225 T2W MR scans are…
Read more about "Deep Learning-based prostate cancer recurrence prediction using prostate MR images"
Prostate MRI performance depends on high-quality imaging. Prostate MRI quality…
Our work is to automate neonatal ventricle segmentation from 3D…
Read more about "Neonatal Cerebral Lateral Ventricle Segmentation from 3D Ultrasound Images"
