Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images

Changwoo Lee, Jongduk Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image quality assessment is important to maintain and improve the imaging system performance, and conducting a human observer study is considered the most desirable approach for the given task because the human makes the diagnostic decision. However, performing a human observer study is time-consuming and expensive. As an alternative method, mathematical model observers to mimic the human observer performance have been proposed. In this work, we proposed convolutional neural network (CNN) based anthropomorphic model observer and compared its performance with human observer and dense difference-of-Gaussian channelized Hotelling observers (D-DOG CHO) for breast tomosynthesis images. The proposed network contained input image, 2D convolution, batch normalization, leaky ReLU, fully connected, and regression layers, and we trained the network using stochastic gradient with momentum (SGDM) optimizer with design parameters, such as filter size and number of filters. For training, validation, and testing data set, anatomical background with 30% volume glandular fraction was generated using the power law spectrum of breast anatomy, and sphere object with a 1 mm diameter was used as a lesion for detection task. In-plane breast tomosynthesis images were obtained using filtered back-projection based tomosynthesis reconstruction. To evaluate detection performance of human observer, D-DOG CHO, and the proposed network, we calculated percent correct (Pc) as a figure of merit. Our results show that the detectability of the proposed network containing 20 number of 11 by 11 convolution filters is most similar to that of human observer.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Sian Taylor-Phillips
PublisherSPIE
ISBN (Electronic)9781510633995
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: 2020 Feb 192020 Feb 20

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11316
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CityHouston
Period20/2/1920/2/20

Bibliographical note

Funding Information:
Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (2018M3A9H6081482, 2018M3A9H6081483, 2018R1A1A1A05077894, 2017M2A2A4A01 070302, 2017M2A2A6A01019663, 2017M2A2A6A02087175).

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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