Organ localization and identification in thoracic CT volumes using 3D CNNs leveraging spatial anatomic relations
| Autor: | James Shackleford, Rajath E. Soans |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Deep learning Detector Posterior probability Pattern recognition Bayes classifier Bayesian inference Convolutional neural network Object detection 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Robustness (computer science) 030220 oncology & carcinogenesis Artificial intelligence business |
| Zdroj: | Medical Imaging: Image Processing |
| DOI: | 10.1117/12.2293801 |
| Popis: | 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 e [r,Θ,Φ]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 94.87p, 95.37p, and 90.76p after the CNN stage, respectively. Introduction of spatial relationship using a Bayes classifier improved the detector accuracies to 95.14p, 96.20p, and 95.15p, 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. |
| Databáze: | OpenAIRE |
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