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Imaging"],"abstract":"<jats:p>Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer\u2019s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi\u2019s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 \u00b1 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.<\/jats:p>","DOI":"10.3390\/jimaging8100259","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T21:10:05Z","timestamp":1663881005000},"page":"259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-1188","authenticated-orcid":false,"given":"Soumick","family":"Chatterjee","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"},{"name":"Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"},{"name":"Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kartik","family":"Prabhu","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahantesh","family":"Pattadkal","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerda","family":"Bortsova","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4760-2263","authenticated-orcid":false,"given":"Chompunuch","family":"Sarasaen","sequence":"additional","affiliation":[{"name":"Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"},{"name":"Institute for Medical Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Dubost","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hendrik","family":"Mattern","sequence":"additional","affiliation":[{"name":"Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marleen","family":"de Bruijne","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands"},{"name":"Department of Computer Science, University of Copenhagen, DK-2100 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Speck","sequence":"additional","affiliation":[{"name":"Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"},{"name":"German Center for Neurodegenerative Disease, 39120 Magdeburg, Germany"},{"name":"Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"N\u00fcrnberger","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"},{"name":"Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany"},{"name":"Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1002\/ana.20505","article-title":"Tertiary microvascular territories define lacunar infarcts in the basal ganglia","volume":"58","author":"Feekes","year":"2005","journal-title":"Ann. 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