Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods

Abstract: We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e.g., structure from motion, to supervise learning in a sparse manner. Consequently, our method requires neither manual labeling nor patient computed tomography (CT) scan in the training and application phases. In a cross-patient experiment using CT scans as groundtruth, the proposed method achieved submillimeter mean residual error. In a comparison study to recent self-supervised depth estimation methods designed for natural video on in vivo sinus endoscopy data, we demonstrate that the proposed approach outperforms the previous methods by a large margin. The source code for this work is publicly available online at https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch.

• The paper demonstrates a self-supervised learning approach using a Siamese CNN for accurate dense depth estimation in monocular endoscopy.
• It introduces novel loss functions that integrate multi-view stereo cues from Structure from Motion to handle photometric variability.
• The framework shows robust cross-patient performance with submillimeter mean residual error, enhancing surgical navigation without manual labeling.

Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods

The paper "Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods" explores an innovative approach to dense depth estimation in minimally invasive surgical environments using endoscopic cameras. The authors address the challenges posed by the absence of pre-operative CT scans and manual labeling, striving to develop a self-supervised learning framework that only requires endoscopic video input. This research builds upon existing work in computer vision and aims to enhance real-time navigation in surgical procedures through improved spatial awareness.

Methodological Overview

The core methodology revolves around a two-branch Siamese neural network architecture, employing convolutional neural networks (CNNs) leveraged with self-supervised signals. The primary contributions highlighted within the paper include:

1. Deep Learning for Depth Estimation in Endoscopy: This method exclusively uses monocular endoscopic imagery for training and application, circumventing traditional requirements such as manual annotations or supplementary imaging modalities like CT scans.
2. Innovative Loss Functions: Authors introduce novel loss functions that integrate multi-view stereo methods, specifically Structure from Motion (SfM), to accommodate the inherent challenges of photometric variability in endoscopic scenes.
3. Generalization to Different Patients and Devices: The framework is validated through cross-patient experiments, demonstrating robust generalization across different patients and endoscopic devices.

Technical Contributions

The research makes significant strides in applying depth estimation to endoscopic images by addressing several technical challenges:

• Sparse Flow Loss and Depth Consistency Loss: These custom-designed loss functions harness sparse depth annotations from SfM to supervise network training. They effectively couple sparse geometric constraints with dense spatial predictions, assisting in robustizing depth predictions against variability in input data.
• Depth Scaling and Flow from Depth Layers: These layers facilitate matching the depth prediction scale to that of SfM-derived measurements, ensuring depth predictions are consistently scaled across frames.
• Self-supervised Training: Unlike typical endoscopy setups requiring extensive manual preparation, this approach paves the way for scalable usage in diverse surgical environments without needing laborious data preparation.

Experimental Validation

Experiments conducted demonstrate compelling performance across multiple randomly selected patients in cross-validation settings. An average submillimeter mean residual error is achieved by comparing the predictions against CT-derived ground-truth models. Additionally, the authors compare their proposed method against existing self-supervised depth estimation techniques like those by Zhou et al. and Yin et al., consistently outperforming them in both quantitative metrics (e.g., absolute relative difference, threshold tests) and qualitative outcomes visualized through 3D reconstructions.

Implications and Future Directions

The practical implications of this work are profound. By eliminating the need for extensive manual labeling and supplementary imaging modalities, there is potential for widespread deployment in surgical navigation systems, maximizing the efficacy of minimally invasive procedures. The method allows integration into existing clinical workflows with minimal overhead, providing an immediate benefit in patient safety and surgical efficiency.

Future research directions may explore extending these self-supervised approaches to other anatomical regions or improve upon the robustness of SfM under severe endoscopic variability. Integration with real-time SLAM systems could also enhance the robustness of depth estimation in highly dynamic and unstructured environments, such as within the human body during surgery.

In conclusion, this paper marks a significant step forward in leveraging computer vision and machine learning for medical applications, challenging traditional paradigms and expanding the capabilities of navigational systems in endoscopic surgeries.