(ISMRM 2024) Toward Task-Based Reconstruction: Evaluating Relationships Between Reconstruction and Object Detection Performance
Meeting Banner
Abstract #1193

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

Natalia Konovalova1, Aniket Tolpadi1,2, Rupsa Bhattacharjee1, Johanna Luitjens1, Felix Gassert1, Paula Giesler1, Sharmila Majumdar1, and Valentina Pedoia1
1Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2University of California, Berkeley, Berkeley, CA, United States

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords

reconstructiondetectionperformancemetricsanomalyreconstructedchallengesliceobjectlearning