Citation: Chatterjee, S.; Prabhu, K.;
Pattadkal, M.; Bortsova, G.; Sarasaen,
C.; Dubost, F.; Mattern, H.; de Bruijne,
M.; Speck, O.; Nürnberger, A. DS6,
Deformation-Aware Semi-Supervised
Learning: Application to Small Vessel
Segmentation with Noisy Training
Data. J. Imaging 2022, 8, 259. https://
doi.org/10.3390/jimaging8100259
Academic Editor: Silvia Liberata Ullo
Received: 10 August 2022
Accepted: 16 September 2022
Published: 22 September 2022
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Article
DS6, Deformation-Aware Semi-Supervised Learning:
Application to Small Vessel Segmentation with Noisy
Training Data
Soumick Chatterjee
1,2,3,
* , Kartik Prabhu
1,†
, Mahantesh Pattadkal
1,†
, Gerda Bortsova
4
,
Chompunuch Sarasaen
3,5
, Florian Dubost
4
, Hendrik Mattern
3
, Marleen de Bruijne
4,6
, Oliver Speck
3,7,8
and Andreas Nürnberger
1,2,8
1
Faculty of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
2
Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg,
39106 Magdeburg, Germany
3
Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
4
Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC,
3015 GD Rotterdam, The Netherlands
5
Institute for Medical Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
6
Department of Computer Science, University of Copenhagen, DK-2100 Copenhagen, Denmark
7
German Center for Neurodegenerative Disease, 39120 Magdeburg, Germany
8
Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany
* Correspondence: soumick.chatterjee@ovgu.de
† These authors contributed equally to this work.
Abstract:
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’s 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’s
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
±
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.
Keywords:
small vessel segmentation; deep learning; MR angiograms; 7 Tesla MRA; TOF-MRA;
high-resolution MRA; imperfect ground-truth
1. Introduction
Small vessels in the brain, such as the Lenticulostriate Arteries (LSA), which supply
blood to the basal ganglia [
1
,
2
], are the terminal branches of the arterial vascular tree.
Pathology of these small vessels is associated with ageing, dementia and Alzheimer’s
J. Imaging 2022, 8, 259. https://doi.org/10.3390/jimaging8100259 https://www.mdpi.com/journal/jimaging
