Publications
Recent deep learning-based contour detection studies show high accuracy in single-class boundary detection problems. However, this performance does not translate well in a multi-class scenario where continuous contours are required. Our research presents CU-Net, a U-Net-based network with residual-net encoders which can produce accurate and uninterrupted contour lines for multiple classes. The critical factor behind this concept is our continuity module, containing an interpolation layer and a novel activation function that converts discrete signals into smooth contours. We find the application of our approach in medical imaging problems like retinal layer segmentation from optical coherence tomography (OCT) scans. We applied our method to an expert annotated OCT dataset of children with sickle-cell disease. To compare with benchmarks, we evaluated our network on DME and HC-MS datasets. We achieved an overall mean absolute distance of 6.48 ± 2.04µM and 1.97 ± 0.89µM, respectively 1.03 and 1.4 times less than the current state-of-the-art.
Ashuta Bhattarai, Chandra Kambhamettu, Jing Jin
2022 IEEE International Conference on Image Processing (ICIP)
Retinal damage in sickle cell disease (SCD) begins with vascular occlusion by sickled red blood cells. Inner retinal thinning due to tissue volume loss is the most common finding in OCT retinal images of SCD patients. Inner retinal thinning from neuronal migration is one of the characteristics of normal fovea. This project aims to develop and validate a deep learning system to detect retinal thickness changes due to sickle cell retinopathy (SCR) using retinal OCT from children with SCD.
Jing Jin, Ashuta Bhattarai, Robin Miller, Edward Kolb, Chandra Kambhamettu