2 citations · 3 across the 4 of their papers we have counts for
5 papers
SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2
Xinrun Chen, Chengliang Wang, Haojian Ning +3
Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D pro…
Snake with Shifted Window: Learning to Adapt Vessel Pattern for OCTA Segmentation
Xinrun Chen, Mei Shen, Haojian Ning +3
Segmenting specific targets or structures in optical coherence tomography angiography (OCTA) images is fundamental for conducting further pathological studies. The retinal vascular…
SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation
Xinrun Chen, Chengliang Wang, Haojian Ning +2
Segmenting specific targets or biomarkers is necessary to analyze optical coherence tomography angiography (OCTA) images. Previous methods typically segment all the targets in an O…
SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks
Chengliang Wang, Xinrun Chen, Haojian Ning +1
In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary. Existing methods typically train on supervised…
An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset
Haojian Ning, Chengliang Wang, Xinrun Chen +1
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficie…