3 citations · 4 across the 6 of their papers we have counts for
6 papers
Universal Wavelet Units in 3D Retinal Layer Segmentation
An D. Le, Hung Nguyen, Melanie Tran +6
This paper presents the first study to apply tunable wavelet units (UwUs) for 3D retinal layer segmentation from Optical Coherence Tomography (OCT) volumes. To overcome the limitat…
Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
An Le, Nehal Mehta, William Freeman +9
In this study, we developed deep learning-based method to classify the type of surgery performed for epiretinal membrane (ERM) removal, either internal limiting membrane (ILM) remo…
Universal Vessel Segmentation for Multi-Modality Retinal Images
Bo Wen, Anna Heinke, Akshay Agnihotri +4
We identify two major limitations in the existing studies on retinal vessel segmentation: (1) Most existing works are restricted to one modality, i.e., the Color Fundus (CF). Howev…
Topology-Preserving Image Segmentation with Spatial-Aware Persistent Feature Matching
Bo Wen, Haochen Zhang, Dirk-Uwe G. Bartsch +3
Topological correctness is critical for segmentation of tubular structures, which pervade in biomedical images. Existing topological segmentation loss functions are primarily based…
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
Abdullah F. Al-Battal, Soan T. M. Duong, Van Ha Tang +5
Multi-phase computed tomography (CT) scans use contrast agents to highlight different anatomical structures within the body to improve the probability of identifying and detecting…
Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging
Yiqian Wang, Alexandra Warter, Melina Cavichini +5
Optical Coherence Tomography (OCT) is one of the most important retinal imaging technique. However, involuntary motion artifacts still pose a major challenge in OCT imaging that co…