Exploiting lamina terminalis appearance and motion in prediction of hydrocephalus using convolutional LSTM network

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buir.contributor.authorAlgın, Oktay
dc.citation.epage369en_US
dc.citation.issueNumber5en_US
dc.citation.spage364en_US
dc.citation.volumeNumber49en_US
dc.contributor.authorSaygılı, Görkem
dc.contributor.authorÖzgöde Yigin, Büşra
dc.contributor.authorGüney, Gökhan
dc.contributor.authorAlgın, Oktay
dc.date.accessioned2023-02-26T06:28:14Z
dc.date.available2023-02-26T06:28:14Z
dc.date.issued2022-08-24
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.description.abstractBackground 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.en_US
dc.description.provenanceSubmitted by Cem Çağatay Akgün (cem.akgun@bilkent.edu.tr) on 2023-02-26T06:28:14Z No. of bitstreams: 1 Exploiting_lamina_terminalis_appearance_and_motion_in_prediction_of_hydrocephalus_using_convolutional_LSTM_network.pdf: 1228634 bytes, checksum: f6dc8de500c6da237cee327454f43121 (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-26T06:28:14Z (GMT). No. of bitstreams: 1 Exploiting_lamina_terminalis_appearance_and_motion_in_prediction_of_hydrocephalus_using_convolutional_LSTM_network.pdf: 1228634 bytes, checksum: f6dc8de500c6da237cee327454f43121 (MD5) Previous issue date: 2022-08-24en
dc.identifier.doi10.1016/j.neurad.2021.02.001en_US
dc.identifier.issn01509861
dc.identifier.urihttp://hdl.handle.net/11693/111750
dc.language.isoEnglishen_US
dc.publisherElsevier Masson s.r.l.en_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.neurad.2021.02.001en_US
dc.source.titleJournal of Neuroradiologyen_US
dc.subjectMRIen_US
dc.subjectLamina terminalisen_US
dc.subjectHydrocephalusen_US
dc.subjectDeep learningen_US
dc.subjectConvLSTMen_US
dc.subjectCNNen_US
dc.titleExploiting lamina terminalis appearance and motion in prediction of hydrocephalus using convolutional LSTM networken_US
dc.typeArticleen_US

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