RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy (2309.09563v1)

Abstract: Unlike in natural images, in endoscopy there is no clear notion of an up-right camera orientation. Endoscopic videos therefore often contain large rotational motions, which require keypoint detection and description algorithms to be robust to these conditions. While most classical methods achieve rotation-equivariant detection and invariant description by design, many learning-based approaches learn to be robust only up to a certain degree. At the same time learning-based methods under moderate rotations often outperform classical approaches. In order to address this shortcoming, in this paper we propose RIDE, a learning-based method for rotation-equivariant detection and invariant description. Following recent advancements in group-equivariant learning, RIDE models rotation-equivariance implicitly within its architecture. Trained in a self-supervised manner on a large curation of endoscopic images, RIDE requires no manual labeling of training data. We test RIDE in the context of surgical tissue tracking on the SuPeR dataset as well as in the context of relative pose estimation on a repurposed version of the SCARED dataset. In addition we perform explicit studies showing its robustness to large rotations. Our comparison against recent learning-based and classical approaches shows that RIDE sets a new state-of-the-art performance on matching and relative pose estimation tasks and scores competitively on surgical tissue tracking.

• The paper introduces a rotation-equivariant architecture using steerable CNNs that ensures precise keypoint detection despite arbitrary camera rotations.
• It implements a self-supervised training pipeline with homographic augmentation, eliminating manual labeling and boosting data efficiency.
• Evaluations on SuPeR and SCARED datasets demonstrate RIDE’s superior matching accuracy and relative pose estimation for improved surgical navigation.

Overview of "RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy"

The paper presents "RIDE," a novel approach in the domain of computer vision for endoscopy that focuses on robust keypoint detection and description under significant rotational changes. The authors leverage the benefits of self-supervised learning and the architectural advancements in group-equivariant convolutional neural networks (CNNs) to introduce a method capable of achieving rotation-equivariant detection and invariant description. This approach addresses the deficiencies in current learning-based methods when dealing with the specific challenges posed by endoscopic imagery, such as unpredictable camera orientations and rotational motions.

Technical Contributions

1. Rotation-Equivariant Architecture: The authors utilize steerable CNNs to implicitly model rotation-equivariance within the framework of RIDE. This design choice is crucial for ensuring precise keypoint detection and descriptor invariance despite arbitrary camera rotations.
2. Self-Supervised Training Pipeline: RIDE is trained in a novel self-supervised manner utilizing homographically augmented datasets, eliminating the need for manual labeling, thus enhancing data efficiency and model applicability to various surgical scenes.
3. Performance Evaluation on Endoscopic Datasets: RIDE has been tested against traditional and state-of-the-art learning-based approaches using the SuPeR and SCARED datasets. The results substantiate its superior performance in matching accuracy and relative pose estimation.

Numerical Results

• RIDE demonstrates significant improvements in performance metrics over the prevalent techniques such as SiLK and classical methods like SIFT when evaluated for matching accuracy and relative pose estimation on the SCARED dataset.
• The model achieves mean matching accuracy superior to both classical methods and modern deep learning approaches across multiple rotational angles.
• For surgical tissue tracking on the SuPeR dataset, RIDE delivered competitive results, highlighting its versatility and robustness in dynamic and deforming scenes, which are common in endoscopy.

Implications and Future Prospects

The implementation of RIDE introduces a promising advancement in the application of geometric computer vision to endoscopy, where it will significantly enhance real-time simultaneous localization and mapping (SLAM) and 3D reconstruction tasks. The integration of steerable CNNs, coupled with a self-supervised learning strategy, positions the RIDE framework advantageously for broader applications in environments with complex visual dynamics and rotational variability.

Future exploration could extend this methodology to directly address the challenges of illumination inconsistencies and tissue deformation more explicitly. Additionally, the adaptability of the model's architecture could be explored further in other domains requiring robust feature detection under rotational transformations. Moreover, the probabilistic fusion of geometric cues and image information could further augment its application in surgical navigation systems.

The findings and approach presented in this paper suggest considerable implications for enhancing the precision and reliability of computer vision systems in minimally invasive surgery, ultimately contributing to improved surgical outcomes and patient recovery experiences.