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Survey details deep learning for endoscopic depth estimation in surgery

By PulseAugur Editorial · [1 sources] ·

This paper provides a comprehensive survey of deep learning techniques for endoscopic depth estimation, a crucial technology for minimally invasive surgery. It reviews current literature from the perspectives of data acquisition, methodologies (both monocular and stereo), and clinical applications. The survey also highlights challenges in clinical implementation and suggests future research directions such as domain adaptation and real-time processing. AI

IMPACT Provides a structured overview of deep learning applications in medical imaging for surgical precision.

RANK_REASON The item is a survey paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details deep learning for endoscopic depth estimation in surgery

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27 / 100
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The item is a survey paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ke Niu, Zeyun Liu, Xue Feng, Heng Li, Naian Xiao, Binghua Su, Qika Lin, Kaize Shi ·

    Endoscopic Depth Estimation Based on Deep Learning: A Survey

    arXiv:2507.20881v3 Announce Type: replace Abstract: Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers in medical imaging, computer vision, and robotic…

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