Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

Ajay Patel; Sil C. van de Leemput; Mathias Prokop; Bram van Ginneken; Rashindra Manniesing

doi:10.1117/12.2254022

3 March 2017 Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

Ajay Patel, Sil C. van de Leemput, Mathias Prokop, Bram van Ginneken, Rashindra Manniesing

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013420 (2017) https://doi.org/10.1117/12.2254022
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 ± 0.01 and mean absolute volume difference of 4.77 ± 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

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Citation Download Citation

Ajay Patel, Sil C. van de Leemput, Mathias Prokop, Bram van Ginneken, and Rashindra Manniesing "Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013420 (3 March 2017); https://doi.org/10.1117/12.2254022

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