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Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans

Authors :
Neubauer, Antonia; https://orcid.org/0000-0001-9820-3735
Li, Hongwei Bran
Wendt, Jil
Schmitz-Koep, Benita
Menegaux, Aurore
Schinz, David
Menze, Bjoern
Zimmer, Claus
Sorg, Christian
Hedderich, Dennis M
Neubauer, Antonia; https://orcid.org/0000-0001-9820-3735
Li, Hongwei Bran
Wendt, Jil
Schmitz-Koep, Benita
Menegaux, Aurore
Schinz, David
Menze, Bjoern
Zimmer, Claus
Sorg, Christian
Hedderich, Dennis M
Source :
Neubauer, Antonia; Li, Hongwei Bran; Wendt, Jil; Schmitz-Koep, Benita; Menegaux, Aurore; Schinz, David; Menze, Bjoern; Zimmer, Claus; Sorg, Christian; Hedderich, Dennis M (2022). Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans. Clinical Neuroradiology, 32(3):665-676.
Publication Year :
2022

Abstract

Purpose Intrauterine claustrum and subplate neuron development have been suggested to overlap. As premature birth typically impairs subplate neuron development, neonatal claustrum might indicate a specific prematurity impact; however, claustrum identification usually relies on expert knowledge due to its intricate structure. We established automated claustrum segmentation in newborns. Methods We applied a deep learning-based algorithm for segmenting the claustrum in 558 T2-weighted neonatal brain MRI of the developing Human Connectome Project (dHCP) with transfer learning from claustrum segmentation in T1-weighted scans of adults. The model was trained and evaluated on 30 manual bilateral claustrum annotations in neonates. Results With only 20 annotated scans, the model yielded median volumetric similarity, robust Hausdorff distance and Dice score of 95.9%, 1.12 mm and 80.0%, respectively, representing an excellent agreement between the automatic and manual segmentations. In comparison with interrater reliability, the model achieved significantly superior volumetric similarity (p = 0.047) and Dice score (p < 0.005) indicating stable high-quality performance. Furthermore, the effectiveness of the transfer learning technique was demonstrated in comparison with nontransfer learning. The model can achieve satisfactory segmentation with only 12 annotated scans. Finally, the model’s applicability was verified on 528 scans and revealed reliable segmentations in 97.4%. Conclusion The developed fast and accurate automated segmentation has great potential in large-scale study cohorts and to facilitate MRI-based connectome research of the neonatal claustrum. The easy to use models and codes are made publicly available.

Details

Database :
OAIster
Journal :
Neubauer, Antonia; Li, Hongwei Bran; Wendt, Jil; Schmitz-Koep, Benita; Menegaux, Aurore; Schinz, David; Menze, Bjoern; Zimmer, Claus; Sorg, Christian; Hedderich, Dennis M (2022). Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans. Clinical Neuroradiology, 32(3):665-676.
Notes :
application/pdf, info:doi/10.5167/uzh-214466, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1443043404
Document Type :
Electronic Resource