Deep learning model observer for 4-alternative forced choice in digital breast tomosynthesis

Seungyeon Choi; Sunghoon Choi; Young-Wook Choi; Hee-Joung Kim

doi:10.1117/12.2549504

16 March 2020 Deep learning model observer for 4-alternative forced choice in digital breast tomosynthesis

Seungyeon Choi, Sunghoon Choi, Young-Wook Choi, Hee-Joung Kim

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113125I (2020) https://doi.org/10.1117/12.2549504
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

The purpose of this study is to investigate deep learning model observer (DLMO) using classification network for various sizes of mass detection tasks in digital breast tomosynthesis (DBT). Different tube current and sweep angular range acquisition settings were repeated to acquire different DBT images. As a result, a total number of 66 cases of four alternative forced choice (4AFC) reading was undertaken to the human observer and DLMO. The images of spheroidal mass with different sizes of 1.8, 2.3, 3.1, 3.9, 4.7, and 6.3 mm in the target slab of CIRS breast phantom were cropped by 200x200 size for their region of interest (ROI). A percentage of correct responses (𝑃_c) was measured at the end of each human observer test and compared by the accuracy of prediction using DLMO. The results indicated that our proposed DLMO showed the averaged training accuracy 93% using 60 testing datasets after 50 epochs of training using 204 input training datasets. Both the accuracy and loss function in training session of categorical cross-entropy reached a plateau after 35 epochs. The sensitivity and specificity of the testing data from DLMO showed 78% and 98%, respectively, which is comparable to the 𝑃_c, 0.89, on 4AFC test from the human observer. Although the current number of datasets is too small to apply in clinical trials, the authors considered that our DLMO on the phantom study is useful for initial trial of analysis on DBT images.

Citation Download Citation

Seungyeon Choi, Sunghoon Choi, Young-Wook Choi, and Hee-Joung Kim "Deep learning model observer for 4-alternative forced choice in digital breast tomosynthesis", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113125I (16 March 2020); https://doi.org/10.1117/12.2549504

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KEYWORDS

Digital breast tomosynthesis

Breast

Prototyping

Clinical trials

Molybdenum

Binary data

Convolution

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