Volumetric Image Segmentation of Lizard Brains

Sammanfattning: Accurate measurement brain region volumes are important in studying brain plasticity, which brings insight into the fundamental mechanisms in animal, memory, cognitive, and behavior research. The traditional methods of brain volume measurements are ellipsoid or histology. In this study, micro-computed tomography (micro-CT) method was used to achieve more accurate results. However, manual segmentation of micro-CT images is time consuming, hard to reprodu-ce, and has the risk of human error. Automatic image segmentation is a faster method for obtaining the segmentations and has the potential to provide eciency, reliability, repeatability, and scalability. Different methods are tested and compared in this thesis. In this project, 29 micro-CT scans of lizard heads were used and measurements of the volumes of 6 dierent brain regions was of interest. The lizard heads were semi-manually segmented into 6 regions and three open-source segmentation algorithms were compared, one atlas-based algorithm and two deep-learning-based algorithms. Dierent number of training data were quantitatively compared for deep-learning methods from all three orientations (sagittal, horizontal and coronal). Data augmentation was tested and compared, as well. The comparison shows that the deep-learning algorithms provided more accurate results than the atlas-based algorithm. The results also demonstrated that in the sagittal plane, 5 manually segmented images for training are enough to provide resulting predictions with high accuracy (dice score 0.948). Image augmentation was shown to improve the accuracy of the segmentations but a unique dataset still plays an important role. In conclusion, the results show that the manual segmentation work can be reduced drastically by using deep learning for image segmentation.