Deformable medical image registration and automatic segmentation are essential
procedures in many image analysis applications, such as in early detection and
monitoring of fetal and neonatal brain development. Nowadays deep learning can
help clinicians in performing these tasks achieving similar or even better results
than classical approaches but much faster. V.Comte et al., 2023 presented a novel
unsupervised segmentation method based on multi-atlas segmentation, which in
fact, avoids the ...
Deformable medical image registration and automatic segmentation are essential
procedures in many image analysis applications, such as in early detection and
monitoring of fetal and neonatal brain development. Nowadays deep learning can
help clinicians in performing these tasks achieving similar or even better results
than classical approaches but much faster. V.Comte et al., 2023 presented a novel
unsupervised segmentation method based on multi-atlas segmentation, which in
fact, avoids the need for a large annotated dataset to train the DL network [1].
This work aims to modify this pipeline by incorporating a biomechanical model to
serve as a constraint during CNN training. Biomechanical models that can describe
brain deformation can be utilized for DL regularization purposes. Furthermore, this
work also tries to access the local shear modulus of the fetal brain, an important
measurement that otherwise is nearly inaccessible through other techniques. The
best validation Dice score obtained in this work was 0.924 and outperforms some
of the state-of-the-art registration pipelines that have been developed, where their
validation Dice score was around 0.862 [2] and 0.858 [3]. Moreover, this work was
used to obtain local shear and bulk modulus maps of the fetal bran where the
cerebellum, the brainstem and the thalamus were found to be the stiffer regions in
the brain. Additionally, the results obtained in this project indicate that the white
matter exhibits slightly greater stiffness compared to the cortex.
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