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Exploiting lamina terminalis appearance and motion in prediction of hydrocephalus using convolutional LSTM network - 16/02/21

Doi : 10.1016/j.neurad.2021.02.001 
Görkem Saygılı a, b, 1, , Büşra Özgöde Yigin a, 1, Gökhan Güney a, Oktay Algın c, d, e
a Department of Biomedical Engineering, Ankara University, Ankara, Turkey 
b Department of Interdisciplinary Neuroscience, Health Science Institute, Ankara University, Ankara, Turkey 
c Department of Radiology, City Hospital, Bilkent, Ankara, Turkey 
d Department of Radiology, Yildirim Beyazit University, Ankara, Turkey 
e National MR Research Center (UMRAM), City Hospital, Bilkent University, Ankara, Turkey 

Corresponding author at: Department of Biomedical Engineering, Ankara University, Ankara, Turkey.Department of Biomedical Engineering, Ankara UniversityAnkaraTurkey
En prensa. Pruebas corregidas por el autor. Disponible en línea desde el Tuesday 16 February 2021

Graphical abstract




El texto completo de este artículo está disponible en PDF.

Highlights

It was found that exploiting the movement of lamina terminalis could contribute to the diagnosis of hydrocephalus using deep learning algorithms.
Two deep learning models on hydrocephalus prediction were compared.
Using the ConvLSTM model, accuracy of more than 80.7% was obtained with an AUC score of 0.81 using only images of the lamina terminalis membrane region in the classification of hydrocephalus.

El texto completo de este artículo está disponible en PDF.

Abstract

Background

Evaluation of the lamina terminalis (LT) is crucial for non-invasive evaluation of the CSF diversion for the treatment of hydrocephalus. Together with deep learning algorithms, morphological and physiological analyses of the LT may play an important role in the management of hydrocephalus.

Aim

We aim to show that exploiting the motion of LT can contribute to the evaluation of hydrocephalus using deep learning algorithms.

Methods

The dataset contains 61 True-fisp data with routine sequences 37 of which are labeled as ‘hydrocephalus’ and the others as ‘normal condition’. A fifteen-year experienced neuroradiologist divided data into two groups. The first group, ‘hydrocephalus’, consists of patients with typical MRI findings (ventriculomegaly, enlargement of the third ventricular recesses and lateral ventricular horns, decreased mamillo-pontine distance, reduced frontal horn angle, thinning/elevation of the corpus callosum, and non-dilated convexity sulci), and the second group contains samples that did not show any symptoms or neurologic abnormality and labeled as ‘normal condition’. The region of interest was determined by the radiologist supervisor to cover the LT. To achieve our purpose, we used both spatial and spatio-temporal analysis with two different deep learning architectures. We utilized Convolutional Neural Networks (CNN) for spatial and Convolutional Long Short-Term Memory (ConvLSTM) models for spatio-temporal analysis using an ROI around LT on sagittal True-fisp images.

Results

Our results show that 80.7% classification accuracy was achieved with the ConvLSTM model exploiting LT motion, whereas 76.5% and 71.6% accuracies were obtained by the 2D CNN model using all frames, and only the first frame from only spatial information, respectively.

Conclusion

We suggest that the motion of the LT can be used as an additional attribute to the spatial information to evaluate the hydrocephalus.

El texto completo de este artículo está disponible en PDF.

Keywords : MRI, Lamina terminalis, Hydrocephalus, Deep learning, ConvLSTM, CNN


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