Evaluation of few-shot detection of head and neck anatomy in CT

Kyungeun Lee; Jihoon Cho; Jiye Lee; Fangxu Xing; Xiaofeng Liu; Hyungjoon Bae; Kyungsu Lee; Jae Youn Hwang; Jinah Park; Georges El Fakhri; Kyung-Wook Jee; Jonghye Woo

doi:10.1117/12.3006895

3 April 2024 Evaluation of few-shot detection of head and neck anatomy in CT

Kyungeun Lee, Jihoon Cho, Jiye Lee, Fangxu Xing, Xiaofeng Liu, Hyungjoon Bae, Kyungsu Lee, Jae Youn Hwang, Jinah Park, Georges El Fakhri, Kyung-Wook Jee, Jonghye Woo

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292716 (2024) https://doi.org/10.1117/12.3006895
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The detection of anatomical structures in medical imaging data plays a crucial role as a preprocessing step for various downstream tasks. It, however, poses a significant challenge due to highly variable appearances and intensity values within medical imaging data. In addition, there is a scarcity of annotated datasets in medical imaging data, due to high costs and the requirement for specialized knowledge. These limitations motivate researchers to develop automated and accurate few-shot object detection approaches. While there are generalpurpose deep learning models available for detecting objects in natural images, the applicability of these models for medical imaging data remains uncertain and needs to be validated. To address this, we carry out an unbiased evaluation of the state-of-the-art few-shot object detection methods for detecting head and neck anatomy in CT images. In particular, we choose Query Adaptive Few-Shot Object Detection (QA-FewDet), Meta Faster R-CNN, and Few-Shot Object Detection with Fully Cross-Transformer (FCT) methods and apply each model to detect various anatomical structures using novel datasets containing only a few images, ranging from 1- to 30-shot, during the fine-tuning stage. Our experimental results, carried out under the same setting, demonstrate that few-shot object detection methods can accurately detect anatomical structures, showing promising potential for integration into the clinical workflow.

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Kyungeun Lee, Jihoon Cho, Jiye Lee, Fangxu Xing, Xiaofeng Liu, Hyungjoon Bae, Kyungsu Lee, Jae Youn Hwang, Jinah Park, Georges El Fakhri, Kyung-Wook Jee, and Jonghye Woo "Evaluation of few-shot detection of head and neck anatomy in CT", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292716 (3 April 2024); https://doi.org/10.1117/12.3006895

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