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Point-Guided Latent Diffusion Model for Novel View Synthesis in Laparoscopic Liver Surgery.

Wenzhe Tang¹, Tao Chen¹, Yamid Espinel²

¹School of Computer Science, Faculty of Engineering and Physical Sciences University of Leeds Leeds UK.

Healthcare Technology Letters

|November 21, 2025

Summary

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Camera augmentation: enabling uncalibrated stereo matching of minimally invasive surgery images by training from the wealth of public synthetic image datasets.

International journal of computer assisted radiology and surgery·2026

This study introduces a novel point-guided latent diffusion model for generating accurate surgical video views. The method enhances surgical scene reconstruction by overcoming challenges like occlusions and complex anatomy.

Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Synthesizing novel views in laparoscopic liver surgery is difficult due to occlusions and limited fields of view.
Existing diffusion-based video synthesis methods struggle with accuracy in complex surgical scenarios.

Purpose of the Study:

To develop a method for generating high-quality intermediate frames in laparoscopic liver surgery from sparse input.
To improve the accuracy and coherence of surgical scene reconstruction.

Main Methods:

Proposed a point-guided latent diffusion model integrating 3D point clouds from dense stereo matching.
Implemented an adaptive camera trajectory planning strategy using next-best-view algorithms to handle occlusions.
Introduced a spatial-transformer enhanced decoder to preserve anatomical details and reduce visual artifacts.

Keywords:

computer vision image reconstruction liver medical image processing

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Main Results:

The point-guided latent diffusion model demonstrated superior performance in generating visually coherent and structurally accurate novel views.
Validated effectiveness on the P2ILF challenge dataset for clinically relevant surgical scene reconstruction.
Successfully addressed challenges of occlusions, complex anatomical shapes, and limited fields of view.

Conclusions:

The proposed method significantly enhances the quality of surgical scene reconstruction in laparoscopic liver surgery.
This approach offers a promising solution for improving visualization and navigation during minimally invasive procedures.
The integration of diffusion models with geometric cues provides a robust framework for complex surgical video synthesis.