Implementation of CNN-based multi-slice model observer for 3D cone beam CT

Gihun Kim, Jongduk Baek

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


In this study, we implement CNN-based multi-slice model observer for 3D CBCT images and compare it with a conventional linear model observer. To evaluate detection performance of the model observer, we considered SKE/BKS four alternative detection task for 3D CBCT images. To generate training and testing datasets, we used a power law spectrum to generate anatomical noise structure. Generated anatomical noise was reconstructed by using FDK algorithm with a CBCT geometry. We employed msCHO and vCHO with LG channels as a comparative linear model observer. We implemented CNN-based multi-slice model observer mimicked msCHOa, which was composed of multiple CNNs. Each CNN consisted of convolutional operator, the batch normalization, a Leaky-ReLU as activation function, and had the following characteristics. (1) To reduce the number of variables, we used full convolutional network and set the filter size as 3×3. (2) Since downscaling layer ignores high frequency components, we did not use any kind of downscaling layer. We used ADAM optimizer and the cross-entropy loss function to train the network. We compared the detection performance of CNN-based multislice model observer, vCHO and msCHO using 1,000 trial cases when the number of slices was three, five and seven. For all numbers of slices, CNN-based multi-slice model observer provided higher detection performance than conventional linear model observers. CNN-based multi-slice model observer required more than 50,000 signal-present and signal-absent images to provide optimized performance, while msCHO required about 5,000 image pairs. Strategy to reduce the amount of training dataset will be a future research topic.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Sian Taylor-Phillips
ISBN (Electronic)9781510640276
Publication statusPublished - 2021
EventMedical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment - Virtual, Online
Duration: 2021 Feb 152021 Feb 19

Publication series

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


ConferenceMedical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment
CityVirtual, Online

Bibliographical note

Publisher Copyright:
Copyright © 2021 SPIE.

All Science Journal Classification (ASJC) codes

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


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