Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images

Zixin Yang; Richard A. Simon; Cristian A. Linte

doi:10.1117/1.JMI.10.4.045001

14 July 2023 Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images

Zixin Yang, Richard A. Simon, Cristian A. Linte

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 4, 045001 (July 2023). https://doi.org/10.1117/1.JMI.10.4.045001

Abstract

Purpose

Stereo matching methods that enable depth estimation are crucial for visualization enhancement applications in computer-assisted surgery. Learning-based stereo matching methods have shown great promise in making accurate predictions on laparoscopic images. However, they require a large amount of training data, and their performance may be degraded due to domain shifts.

Approach

Maintaining robustness and improving the accuracy of learning-based methods are still open problems. To overcome the limitations of learning-based methods, we propose a disparity refinement framework consisting of a local disparity refinement method and a global disparity refinement method to improve the results of learning-based stereo matching methods in a cross-domain setting. Those learning-based stereo matching methods are pre-trained on a large public dataset of natural images and are tested on two datasets of laparoscopic images.

Results

Qualitative and quantitative results suggest that our proposed disparity framework can effectively refine disparity maps when they are noise-corrupted on an unseen dataset, without compromising prediction accuracy when the network can generalize well on an unseen dataset.

Conclusions

Our proposed disparity refinement framework could work with learning-based methods to achieve robust and accurate disparity prediction. Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, the incorporation of the proposed disparity refinement framework into existing networks will contribute to improving their overall accuracy and robustness associated with depth estimation.

Citation Download Citation

Zixin Yang, Richard A. Simon, and Cristian A. Linte "Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images," Journal of Medical Imaging 10(4), 045001 (14 July 2023). https://doi.org/10.1117/1.JMI.10.4.045001

Received: 29 March 2023; Accepted: 5 July 2023; Published: 14 July 2023

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KEYWORDS

Education and training

Endoscopy

Laparoscopy

Light sources and illumination

Error analysis

Equipment

Data modeling

Purpose

Approach

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Conclusions

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