Advancing Segmentation of Intracranial Structures in Brain Imaging

dc.contributorAalto Universityen
dc.contributor.advisorIlmoniemi, Risto, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
dc.contributor.authorThanellas, Antonios
dc.contributor.departmentNeurotieteen ja lääketieteellisen tekniikan laitosfi
dc.contributor.departmentDepartment of Neuroscience and Biomedical Engineeringen
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorHämäläinen, Matti, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
dc.description.abstractThere are several challenges and crossroads that a researcher encounters and must navigate while conducting studies in the field of anatomical neuroimaging. These include 1) shortage of large datasets for algorithm testing and the limitations associated with small datasets, 2) finding strategies to make established algorithms more efficient, and 3) development of algorithms robust enough to generalize across different hardware and different acquisition settings. This thesis seeks to address these challenges. It explores the impact of limited datasets on volumetric analyses from Magnetic Resonance Images, introduces innovative approaches for locating intracranial structures and pathologies from Magnetic Resonance (MR) and Computed Tomography (CT) images, and examines how data selection influences analyses and algorithm performance in diverse scenarios. One aspect of the present research quantifies and proposes strategies to mitigate the influence of biases, confounders, and random variations that frequently arise in brain volumetric analyses with limited datasets. The findings emphasise the effectiveness of specific metrics in accurately distinguishing between healthy and non-healthy subjects even in the presence of bias, confounders, or random variation. This thesis also introduces a novel method for segmenting the brain from MR images. This method combines segmentation fusion with a marker-controlled watershed transform and utilizes predictions from established segmentation methods as input. Results demonstrate superior performance compared to the conventional segmentation techniques and other meta-algorithms. In the field of machine learning, the research recommends effective approaches for creating training data with the aim of segmenting intracranial blood from CT images. A neural network developed for this purpose demonstrates potential in segmenting intracranial blood from head CT images while preserving generalizability. In conclusion, the challenges posed by limited datasets require special considerations as they impact the development of both machine learning and classical image processing methods for segmenting structures within the head. While classical image processing methods may see reduced usage on their own, they will likely be increasingly integrated with machine learning approaches.en
dc.format.extent104 + app. 40
dc.identifier.isbn978-952-64-1723-3 (electronic)
dc.identifier.isbn978-952-64-1722-6 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.opnTohka, Jussi, Prof., University of Eastern Finland, Finland
dc.publisherAalto Universityen
dc.relation.haspart[Publication 1]: Thanellas, Antonios; Peura, Heikki; Lavinto, Mikko; Ruokola, Tomi; Vieli, Moira; Staartjes, Victor; Winklhofer, Sebastian; Serra, Karlo;Regli, Luca, Korja, Miikka. Development and external validation of a deep learning algorithm to identify and localize subarachnoid hemorrhage on CT scans. In: Neurology, 2023. DOI: 10.1212/WNL.0000000000201710
dc.relation.haspart[Publication 2]: Thanellas, Antonios; Peura, Heikki; Wennervirta, Jenni; Korja, Miikka. 2021. Foundations of brain image segmentation: Pearls and pitfalls in segmenting intracranial blood on computed tomography images. In: Springer Book Series Acta Neurochirurgica Supplements, 2022. DOI: 10.1007/978-3-030-85292-4_19
dc.relation.haspart[Publication 3]: Thanellas, Antonios; Pollari, Mika; Alhonnoro, Tuomas; Lilja Mikko. 2016. Brain extraction from MR images using a combination of segmentation fusion and marker-controlled watershed transform. In: Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD), Strasbourg 29 Oct.–6 Nov. 2016. IEEE. Pages 1–4. ISBN electronic 978-1-5090-1642-6. DOI: 10.1109/NSSMIC.2016.8069542
dc.relation.haspart[Publication 4]: Thanellas, Antonios; Pollari, Mika. 2010 Sensitivity of volumetric brain analysis to systematic and random errors. In: 23rd International Symposium on Computer-Based Medical Systems (CBMS), Perth, Australia 12–15 Oct. 2010. IEEE. Pages 238–242. ISBN electronic 978-1-4244-9168-1. DOI: 10.1109/CBMS.2010.6042648
dc.relation.ispartofseriesAalto University publication series DOCTORAL THESESen
dc.revTohka, Jussi, Prof., University of Eastern Finland, Finland
dc.revYendiki, Anastasia, Assoc. Prof., Harvard Medical School, United States
dc.subject.keywordmagnetic resonance imagingen
dc.subject.keywordcomputed tomographyen
dc.subject.keyworddeep learningen
dc.subject.keywordintracranial haemorrhageen
dc.subject.keywordanatomical neuroimagingen
dc.subject.otherMedical sciencesen
dc.titleAdvancing Segmentation of Intracranial Structures in Brain Imagingen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
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