Cystoscopic depth estimation using gated adversarial domain adaptation.

Area of Science:

• Medical Imaging
• Computer Vision
• Robotics

Background:

• Monocular depth estimation is vital for scene understanding in fields like automotive and medicine.
• Traditional stereo-based methods fail in endoscopic settings due to lack of rigid environments and ground truth data.
• Cystoscopy presents unique challenges for depth prediction due to these limitations.

Purpose of the Study:

• To develop a robust monocular depth estimation technique for endoscopic procedures, specifically cystoscopies.
• To overcome the challenge of acquiring ground truth depth data for training machine learning models.
• To adapt synthetic data for training on real-world endoscopic images.

Main Methods:

• A synthetic cystoscopic environment was created to generate initial depth information.
• Domain adaptation techniques were employed to bridge the gap between synthetic and real image domains.
• Gated residual blocks were integrated to enhance network stability during adversarial training.

Main Results:

• The developed method successfully predicts depth values from real cystoscopic videos.
• Gated residual blocks demonstrated effectiveness in preventing mode collapse during adversarial training.
• The approach shows promise for improving surgical navigation and analysis in endoscopic procedures.

Conclusions:

• This work presents a viable solution for monocular depth estimation in challenging endoscopic domains.
• The use of synthetic data and domain adaptation offers a pathway for training models without ground truth.
• The integration of gated residual blocks enhances the stability and reliability of the depth prediction network.