AI RESEARCH
Attenuation-Resilient Alternating Optimization for Laparoscopic Liver Landmark Detection
arXiv CS.CV
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ArXi:2605.26630v1 Announce Type: new Liver surface landmark detection is a fundamental prerequisite for anatomical guidance in laparoscopic liver surgery. However, it remains unreliable in practice due to two pervasive challenges: illumination attenuation in underexposed regions and the structural mismatch between pixel-wise localization and continuous curvilinear geometry. To address these limitations, we propose A2ONet, an attenuation-resilient alternating optimization network for robust liver landmark detection.