Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision

TL;DR

This work introduces a novel approach, KeyBot, specifically designed to identify and correct significant and typical errors in existing models, akin to user revision, by characterizing typical error types and using simulated errors for training.

Abstract

Recent advances in interactive keypoint estimation methods have enhanced accuracy while minimizing user intervention. However, these methods require user input for error correction, which can be costly in vertebrae keypoint estimation where inaccurate keypoints are densely clustered or overlap. We introduce a novel approach, KeyBot, specifically designed to identify and correct significant and typical errors in existing models, akin to user revision. By characterizing typical error types and using simulated errors for training, KeyBot effectively corrects these errors and significantly reduces user workload. Comprehensive quantitative and qualitative evaluations on three public datasets confirm that KeyBot significantly outperforms existing methods, achieving state-of-the-art performance in interactive vertebrae keypoint estimation. The source code and demo video are available at: https://ts-kim.github.io/KeyBot/

Figures (16)

• Figure 1: Vertebrae keypoint refinement results on BUU-LA klinwichit2023buu and AASCE spinewebdataset. Initial predictions exhibit significant errors in vertebra shapes due to difficulty identifying individual vertebrae. KeyBot effectively reduces these errors without user input.
• Figure 2: Error types in vertebrae keypoint estimation, illustrated on the AASCE dataset.
• Figure 3: Overview of (a) the interactive keypoint estimation and (b) the KeyBot framework. The interaction model generates initial keypoint predictions from an image, which are then revised by users, leading to updated results. This approach requires user input for error correction. In contrast, KeyBot offers a cost-efficient feedback mechanism via automated, rapid, and iterative refinement without user input. It independently identifies and corrects major errors, allowing users to focus on the final adjustments.
• Figure 4: Training overview of KeyBot. KeyBot consists of two main components, the detector and the corrector. (a) Synthetic errors are introduced to the groundtruth keypoints, creating inaccurate keypoints and corresponding anomaly labels. (b) The detector is trained to discern whether each input keypoint is accurate or not. (c) The corrector is trained to refine these inaccurate keypoints accurately.
• Figure 5: Implementing realistic estimation inaccuracy simulations for training KeyBot.