Abstract
Depth estimation in colonoscopy images provides geometric clues for downstream medical analysis tasks, such as polyp detection, 3D reconstruction, and diagnosis. Recently, deep learning technology has made significant progress in monocular depth estimation for natural scenes. However, without sufficient ground truth of dense depth maps for colonoscopy images, it is significantly challenging to train deep neural networks for colonoscopy depth estimation. In this paper, we propose a novel approach that makes full use of both synthetic data and real colonoscopy videos. We use synthetic data with ground truth depth maps to train a depth estimation network with a generative adversarial network model. Despite the lack of ground truth depth, real colonoscopy videos are used to train the network in a self-supervision manner by exploiting temporal consistency between neighboring frames. Furthermore, we design a masked gradient warping loss in order to ensure temporal consistency with more reliable correspondences. We conducted both quantitative and qualitative analysis on an existing synthetic dataset and a set of real colonoscopy videos, demonstrating the superiority of our method on more accurate and consistent depth estimation for colonoscopy images.
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We acknowledge funding from National Natural Science Foundation of China under Grants 61976007 and 62076230.
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Cheng, K., Ma, Y., Sun, B., Li, Y., Chen, X. (2021). Depth Estimation for Colonoscopy Images with Self-supervised Learning from Videos. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_12
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