Toward Task-Based Reconstruction: Evaluating Relationships Between Reconstruction and Object Detection Performance

Motivation: Traditional medical image reconstruction emphasizes standard metrics, potentially overlooking optimization for downstream tasks like segmentation and anomaly detection. Goal(s): Our study investigates the relationship between standard reconstruction and object detection metrics. Approach: We trained a Faster R-CNN detector for meniscal anomalies, addressing class imbalance and implementing a custom detection-specific augmentation protocol. Results: Evaluation on reconstructed datasets revealed that reconstruction quality was associated with true predictions but had a limited impact on overall detection performance, while boxes-based reconstruction metrics showed no correlation with prediction outcomes. These findings underscore the importance of considering associations between standard reconstruction and downstream task metrics when optimizing end-to-end pipelines. Impact: Evaluation of standard reconstruction metrics, sliced by object detection outcomes, revealed a significant association between reconstruction and detection performance, emphasizing the utility of this approach in assessing task-based reconstruction.