Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

TL;DR

This paper introduces a reinforcement learning-based framework that dynamically optimizes target precision in heatmap regression for anatomical landmark detection, improving accuracy and stability across medical imaging tasks.

Contribution

It proposes a novel learning-to-learn approach that jointly optimizes neural network parameters and target precision, avoiding manual heuristic settings.

Findings

Enhanced localization accuracy in ultrasound and X-ray datasets

Improved training stability and adaptability

Demonstrated effectiveness across multiple medical imaging modalities

Abstract

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anatomy with the likelihood maps (i.e., heatmaps). However, most current solutions overlook another essence of heatmap regression, the objective metric for regressing target heatmaps and rely on hand-crafted heuristics to set the target precision, thus being usually cumbersome and task-specific. In this paper, we propose a novel learning-to-learn framework for landmark detection to optimize the neural network and the target precision simultaneously. The pivot of this work is to leverage the reinforcement learning (RL) framework to search objective metrics for regressing multiple heatmaps dynamically during the training process, thus avoiding setting problem-specific target precision. We also introduce an early-stop strategy for active termination of the RL agent's interaction that adapts the optimal precision for separate targets considering exploration-exploitation tradeoffs. This approach shows better stability in training and improved localization accuracy in inference. Extensive experimental results on two different applications of landmark localization: 1) our in-house prenatal ultrasound (US) dataset and 2) the publicly available dataset of cephalometric X-Ray landmark detection, demonstrate the effectiveness of our proposed method. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.