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Training Set Preparation for Deep Model Learning Inpatients with Ischemic Brain Lesions and Gender Identity Disorder

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Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14126))

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Abstract

Increasing number of stress related cerebrovascular insults becomes alerting younger involved as well. Cerebrovascular diseases are the most common cause of functional disabilities, stroke being first, of which 87% are ischemic strokes in the USA. The cause of ischemia likely lies in cerebral small vessel disease, which includes lacunar infarcts, microbleeds, white matter lesions (WML) and enlarged perivascular spaces. The pathophysiology of ischemic WML is unclear, despite the development of radiological markers. The gold standard in the evaluation of WML is a volumetric analysis using magnetic resonance imaging (MRI).

The aim of this study was training set preparation and volumetric analysis of multiple or solitary WML due to ischemic changes in patients with depressions and comparing it with a control group without white matter lesions and ischemic changes.

We included 20 participants, 10 with WML and 10 controls as part of the process of applying deep machine learning and preparing a training set for the automated detection of WML. Participants under went 1.5 T 3D-T1v, MPRAGE and 3D FLAIR MRI. Images were aligned with MNI space to normalize their intensity. Manual segmentation was then performed as the gold standard for segmentation in MNI space with ITK-SNAP using T1v and FLAIR images.

A summary estimation of all volumes of the image sections was performed. The total volume of all brain lesions was measured without division into hemispheres and their localization. The total volume of the brain was measured and correlated with the same parameters of subjects without noticeable ischemic changes in the brain.

We have shown that ischemic lesions of the cerebrum white matter affect reduced total brain volume. The studies will aid in coming up with better treatment guidelines for preventive health care and management of cerebrovascular diseases to address the specific needs of individuals with gender identity disorders or transgenders.

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Abbreviations

TBV:

total brain volume

WML:

white matter lesions

MRI:

magnetic resonance imaging

CSVD:

Cerebral small vessel disease

CMB:

cerebral microbleeds

DICOM:

digital imaging and communications in medicine

NIfTI:

Neuroimaging Informatics Technology Initiative

WMH:

white matter hyperintensities

PD:

proton density

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Acknowledgment

This research was funded by the National Centre for Research and Development and prepared within the framework of the scientific project “A new Model of medical care with use of modern methods of non-invasive clinical assessment and Telemedicine in patients with heart failure” (STRATEGMED3/305274/8/NCBR/2017).

Author information

Authors and Affiliations

  1. Laboratory for Multimodal Neuroimaging, Institute of Anatomy, Medical Faculty, University of Belgrade, 11000, Belgrade, Serbia

    Ana Starcevic & Boris Vucinic

  2. Łukasiewicz Research Network – Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopianska Str. 73, 30-418, Krakow, Poland

    Ilona Karpiel

Authors
  1. Ana Starcevic
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  2. Boris Vucinic
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  3. Ilona Karpiel
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Corresponding author

Correspondence to Ilona Karpiel .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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None declared.

Appendix (I–VI)

Appendix (I–VI)

See Figs. 1, 2, 3, 4, 5 and 6

Fig. 1.
figure 1

Targeted lesion

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Fig. 2.
figure 2

Lesion probability mask

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Fig. 3.
figure 3

Lesion probability mask

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Fig. 4.
figure 4

Masked lesion

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Fig. 5.
figure 5

Total volume WMH and controls (± SD).

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Fig. 6.
figure 6

Volumetric differences between brains

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Starcevic, A., Vucinic, B., Karpiel, I. (2023). Training Set Preparation for Deep Model Learning Inpatients with Ischemic Brain Lesions and Gender Identity Disorder. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14126. Springer, Cham. https://doi.org/10.1007/978-3-031-42508-0_17

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  • DOI: https://doi.org/10.1007/978-3-031-42508-0_17

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