Learning observer design for object detection under complex background and variant locations: an application in IQ assessment with spectrum shaping technique for ultra-low dose lung imaging

Yougu Yang; Ruikang Zhang; Xiaolu Cai; Zheng Cui; Yi Tian

doi:10.1117/12.2582105

15 February 2021 Learning observer design for object detection under complex background and variant locations: an application in IQ assessment with spectrum shaping technique for ultra-low dose lung imaging

Yougu Yang, Ruikang Zhang, Xiaolu Cai, Zheng Cui, Yi Tian

Author Affiliations +

Proceedings Volume 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment; 115990Y (2021) https://doi.org/10.1117/12.2582105
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

We would propose a Deep Learning based Model Observer (DLMO) to assess performance of computed tomography (CT) images generated by applying tin-filter based spectral shaping technique. The DLMO was constructed based on a simplified VGG neural network trained from scratch. The training and test image datasets were obtained by scanning an anthropomorphic phantom with high-fidelity pulmonary structure at four dose levels with and without tin-filter, respectively. Spherical urethane foams were attached at variant positions of pulmonary tree to mimic ground glass nodule (GGN). These low dose CT scan images were assessed by the trained DLMO for lung nodule detection. The result demonstrated that spectral shaping by tin-filter can provide additional benefits on detection accuracy for certain ultra-low dose level scan (~0.2mGy), but faces challenges for extremely low dose level (~0.05mGy) due to significant noise. For normal dose range (~0.5 to 1mGy), both images from scan with and scan without tin-filter can achieve comparable detection accuracy on mimic GGN objects. A human observer (HO) study performed by 8 experienced CT image quality engineers on the same dataset as a signal-known-exactly (SKE) nodule detection task also indicated similar results.

Conference Presentation

Citation Download Citation

Yougu Yang, Ruikang Zhang, Xiaolu Cai, Zheng Cui, and Yi Tian "Learning observer design for object detection under complex background and variant locations: an application in IQ assessment with spectrum shaping technique for ultra-low dose lung imaging", Proc. SPIE 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 115990Y (15 February 2021); https://doi.org/10.1117/12.2582105

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