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cs.CV2025
S4M: 4-points to Segment Anything
Adrien Meyer, Lorenzo Arboit, Giuseppe Massimiani +3
Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred boundaries make its point prompts…
cs.CV2021★ 1 cited
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers
Jun-En Ding, Chi-Hsiang Chu, Mong-Na Lo Huang +1
Neurodegenerative parkinsonism can be assessed by dopamine transporter single photon emission computed tomography (DaT-SPECT). Although generating images is time consuming, these i…
cs.CV2017★ 51 cited
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Lequan Yu, Jie-Zhi Cheng, Qi Dou +4
Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treat…