• Nina Jacobsen
  • Anne Krogh Nøhr
  • Bolette Dybkjær Hansen
4. semester , Sundhedsteknologi, Kandidat (Kandidatuddannelse)
Objective: The hippocampal subfields are of great interest within research due to a diagnostic connectivity
to neurodegenerative diseases, and increased access to ultra-high field MR imaging has made segmentation
of these subfields feasible. Automatic methods for segmentation of the hippocampal subfields have been
proposed. However, these methods show limitations with respect to segmentation of smaller subfields and
they are very time consuming. In current research, Convolutional Neural Networks (CNN) have been used
to segment brain structures and lesions, and the approach have shown fast and accurate segmentations. The
aim of this study was to develop a CNN based automatic method for hippocampal subfield segmentation
(DeepHSS) and explore the impact of a small dataset.

Method: DeepHSS consists of a preprocessing pipeline followed by a 12 layer, two pathways 3D CNN
trained using dense training. The training set includes 7T TSE MR images acquired from 15 subjects and
associated manual labels delineating six hippocampal subfields and Entorhinal Cortex. DeepHSS was
tested with 10 subjects. The segmentations by DeepHSS were compared to the corresponding manual
labels using the Dice Similarity Coefficient (DSC).

Results: DeepHSS showed fast segmentation of hippocampal subfields with an average foreground DSC
at 0,83±0.03. Highest average segmentation DSC was achieved for Subiculum (0.80±0.05) and Dendate
Gyrus (0.77±0.05) whilst the lowest average segmentation accuracy was found for Entorhinal Cortex
(0.49±0.14). The accuracy of the segmentation increased with the number of training subjects.

Conclusion: Using our automatic hippocampal subfield segmentation method, DeepHSS, we demonstrated CNNs as an efficient method for automatic hippocampal subfield segmentation despite utilisation of a
small dataset. The results were comparable with results obtained using ASHS.
SprogEngelsk
Udgivelsesdato22 jun. 2017
Ekstern samarbejdspartnerCentre for Advanced Imaging, University of Queensland
Steffen Bollmann steffen.bollmann@cai.uq.edu.au
Anden
ID: 259276823