Conference proceeding
Organ Localization & Identification in Thoracic CT Volumes using 3D CNNs Leveraging Spatial Anatomic Relations
MEDICAL IMAGING 2018: IMAGE PROCESSING, v 10574, pp 105741X-105741X-12
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper, we present a model to obtain prior knowledge for organ localization in CT thorax images using three dimensional convolutional neural networks (3D CNNs). Specifically, we use the knowledge obtained from CNNs in a Bayesian detector to establish the presence and location of a given target organ defined within a spherical coordinate system. We train a CNN to perform a soft detection of the target organ potentially present at any point, x = [r, theta, phi](T). This probability outcome is used as a prior in a Bayesian model whose posterior probability serves to provide a more accurate solution to the target organ detection problem. The likelihoods for the Bayesian model are obtained by performing a spatial analysis of the organs in annotated training volumes. Thoracic CT images from the NSCLC Radiomics dataset are used in our case study, which demonstrates the enhancement in robustness and accuracy of organ identification. The average value of the detector accuracies for the right lung, left lung, and heart were found to be 94.87%, 95.37%, and 90.76% after the CNN stage, respectively. Introduction of spatial relationship using a Bayes classifier improved the detector accuracies to 95.14%, 96.20%, and 95.15%, respectively, showing a marked improvement in heart detection. This workflow improves the detection rate since the decision is made employing both lower level features (edges, contour etc) and complex higher level features (spatial relationship between organs). This strategy also presents a new application to CNNs and a novel methodology to introduce higher level context features like spatial relationship between objects present at a different location in images to real world object detection problems.
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Details
- Title
- Organ Localization & Identification in Thoracic CT Volumes using 3D CNNs Leveraging Spatial Anatomic Relations
- Creators
- Rajath Elias Soans - Drexel UniversityJames A. Shackleford - Drexel University
- Contributors
- E D Angelini (Editor)B A Landman (Editor)
- Publication Details
- MEDICAL IMAGING 2018: IMAGE PROCESSING, v 10574, pp 105741X-105741X-12
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 12
- Grant note
- 1553436; 1642380 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000435027500065
- Scopus ID
- 2-s2.0-85047299261
- Other Identifier
- 991019168869504721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Optics
- Radiology, Nuclear Medicine & Medical Imaging