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A subject’s head position in magnetic resonance imaging (MRI) scanners can vary significantly with the imaging environment and disease status. This variation is known to influence the accuracy of skull stripping (SS), a method to extract the brain region from the whole head image, which is an essential initial step to attain high performance in various neuroimaging applications. However, existing SS methods have failed to accommodate this wide range of variation. To achieve accurate, consistent, and fast SS, we introduce a novel two-stage methodology that we call posture correction skull stripping (PCSS): the first involves adjusting the subject’s head angle and position, and the second involves the actual SS to generate the brain mask. PCSS also incorporates various machine learning techniques, such as a weighted loss function, adversarial training from generative adversarial networks, and ensemble methods. Thorough evaluations conducted on five publicly accessible datasets show that the PCSS method outperforms current state-of-the-art techniques in SS performance, achieving an average increase of 1.38 points on the Dice score and demonstrating the contributions of each PCSS component technique.

To build a robust and practical content-based image retrieval (CBIR) system applicable to clinical brain MRI databases, we propose a new framework, disease-oriented image embedding with pseudo-scanner standardization (DI-PSS). It consists of two core techniques: data harmonization to absorb differences caused by different scanning environments and an algorithm to generate low-dimensional embeddings suitable for disease classification. Until now, there have been very few studies aimed at CBIR of brain MRI. Even in the harmonization of scanners, which is an important prerequisite technique for CBIR, only a limited number of studies have been conducted on T1-weighted MRI, which has collected a vast amount of clinical data. Recently proposed methods need to correctly estimate the domain (i.e., dataset, scanner) of each data in advance to remove environment-dependent information from low-dimensional embedding, which is not an easy task. With DI-PSS, each brain image is pseudo-transformed into a brain image taken with a given reference scanner. Then, 3D convolutional autoencoders (3D-CAE) trained with deep metric learning generate low-dimensional embeddings that better reflect the characteristics of the disease. In this study, DI-PSS reduced the variability of distance in low-dimensional embedding between Alzheimer’s disease (AD) and clinically normal (CN) patients, caused by differences in scanners and datasets, by 15.8-22.6% and 18.0-29.9%, respectively, compared to the baseline. This improved the ability of spectral clustering to classify AD and CN by 6.2% in average accuracy and 10.7% in macro-F1. Our method has the advantage of not requiring difficult domain prediction tasks in advance, and can effectively utilize the big data of T1-weighted MR images. Given the potential of the DI-PSS for harmonizing images scanned by MRI scanners that were not used to scan the training data, it is well suited for application to a large number of legacy MRIs captured in heterogeneous environments.