A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates

Alzheimer's Disease and normal aging are both characterized by brain atrophy. The question of whether AD-related brain atrophy represents accelerated aging or a neurodegeneration process distinct from that in normal aging remains unresolved. Moreover, precisely disentangling AD-related brain atrophy from normal aging in a clinical context is complex. In this study, we propose a deformation-based morphometry framework to estimate normal aging and AD-specific atrophy patterns of subjects from morphological MRI scans. We first leverage deep-learning-based methods to create age-dependent templates of cognitively normal (CN) subjects. These templates model the normal aging atrophy patterns in a CN population. Then, we use the learned diffeomorphic registration to estimate the one-year normal aging pattern at the voxel level. We register the testing image to the 60-year-old CN template in the second step. Finally, normal aging and AD-specific scores are estimated by measuring the alignment of this registration with the one-year normal aging pattern. The methodology was developed and evaluated on the OASIS3 dataset with 1,014 T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans were from individuals clinically diagnosed with AD at different stages of clinical severity defined by clinical dementia rating (CDR) scores. The results show that ventricles predominantly follow an accelerated normal aging pattern in subjects with AD. In turn, hippocampi and amygdala regions were affected by both normal aging and AD-specific factors. Interestingly, hippocampi and amygdala regions showed more of an accelerated normal aging pattern for subjects during the early clinical stages of the disease, while the AD-specific score increases in later clinical stages. Our code is freely available at https://github.com/Fjr9516/DBM_with_DL.

AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection

Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering all possible types of diseases. Due to the unavailability of such rich datasets and the high cost of annotations, unsupervised anomaly detection (UAD) methods have been developed aiming to detect the pathologies as deviation from the normality by utilizing the unlabeled healthy image data. However, developed UAD models are often trained with an incomplete distribution of healthy anatomies and have difficulties in preserving anatomical constraints. This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints. Second, we propose an autoinpainting pipeline to automatically detect tumors, replace their appearance with the learned healthy anatomies, and based on that segment the tumoral volumes in a purely unsupervised fashion. Three imaging datasets, including PET, CT, and PET-CT scans of lung tumors and head and neck tumors, are studied as benchmarks for evaluation. Experimental results demonstrate the significant superiority of the proposed method over a wide range of state-of-the-art UAD methods. Moreover, the unsupervised method we propose produces comparable results to a robust supervised segmentation method when applied to multimodal images.

Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT

Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent. However, manual segmentation can be very time consuming and subjective, given the lesions' large variation in shape, size and position. On the other hand, we still lack large manually segmented datasets that could be used for training machine learning-based models for fully automatic segmentation. In this work, we propose a new interactive and user-friendly tool for COVID-19 lesion segmentation, which works by alternating automatic steps (based on level-set segmentation and statistical shape modeling) with manual correction steps. The present software was tested by two different expertise groups: one group of three radiologists and one of three users with an engineering background. Promising segmentation results were obtained by both groups, which achieved satisfactory agreement both between- and within-group. Moreover, our interactive tool was shown to significantly speed up the lesion segmentation process, when compared to fully manual segmentation. Finally, we investigated inter-observer variability and how it is strongly influenced by several subjective factors, showing the importance for AI researchers and clinical doctors to be aware of the uncertainty in lesion segmentation results.