AI-human interactive pipeline with feedback to accelerate medical image annotation

Youngwon Choi; Marlena Garcia; Steven S. Raman; Dieter R. Enzmann; Matthew S. Brown

doi:10.1117/12.2611952

4 April 2022 AI-human interactive pipeline with feedback to accelerate medical image annotation

Youngwon Choi, Marlena Garcia, Steven S. Raman, Dieter R. Enzmann, Matthew S. Brown

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120332U (2022) https://doi.org/10.1117/12.2611952
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

We propose an AI-human interactive pipeline to accelerate medical image annotation of large data sets. This pipeline continuously iterates on three steps. First, an AI system provides initial automated annotations to image analysts. Second, the analysts edit the annotations. Third, the AI system is upgraded with analysts’ feedback, thus enabling more efficient annotation. To develop this pipeline, we propose an AI system and upgraded workflow that is focused on reducing the annotation time while maintaining accuracy. We demonstrated the ability of the feedback loop to accelerate the task of prostate MRI segmentation. With the initial iterations on small batch sizes, the annotation time was reduced substantially.

Citation Download Citation

Youngwon Choi, Marlena Garcia, Steven S. Raman, Dieter R. Enzmann, and Matthew S. Brown "AI-human interactive pipeline with feedback to accelerate medical image annotation", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332U (4 April 2022); https://doi.org/10.1117/12.2611952

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