Machine learning approaches for the study of AD with brain MRI data

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Directors: Roser Sala Llonch, Agnès Pérez MillanThe use of automated or semi-automated approaches based on imaging data has been suggested to support the diagnoses of some diseases. In this context, Machine Learning (ML) appears as a useful emerging tool for this purpose, allowing from feature extraction to automatic classification. Alzheimer Disease (AD) and Frontotemporal Dementia (FTD) are two common and prevalent forms of early-onset dementia with different, but partly overlapping, symptoms and brain patterns of atrophy. Because of the similarities, there is a need to establish an accurate diagnosis and to obtain good markers for prognosis. This work combines both supervised and unsupervised ML algorithms to classify AD and FTD. The data used consisted of gray matter volumes and cortical thicknesses (CTh) extracted from 3TT1 MRI of 44 healthy controls (HC, age: 57.8±5.4 years), 53 Early-Onset Alzheimer Disease patients (EOAD, age: 59.4±4.4 years) and 64 FTD patients (FTD, age: 64.4±8.8 years). A principal component analysis (PCA) of all volumes and thicknesses was performed and a number of principal components (PC) that accumulated at least 80% of the data variance were entered into a Support Vector Machine (SVM). Overall performance was assessed using a 5-fold crossvalidation..

Automatic post-processing of left atrium segmentations

Cardiac anatomical segmentation is an essential step in many clinical applications that require quantitative measures such as myocardial mass and ventricle volume. Traditionally, these segmentations were manually performed by radiologists or clinical specialists, which is a time-consuming task and prone to inter-observer variability. In this context, deep learning (DL) appears as a useful emerging tool for this purpose, allowing for automated and semi-automated segmentations. However, DL models can still produce errors. Because of this, in some cases there is the need for post-processing methods to refine the output and improve the accuracy of the segmentation. This work proposes a denoising autoencoder (DAE) as a post-processing step to remove errors from the left atrium (LA) segmentations output by a U-Net model. The data used consisted of 82 ground truth LA masks segmented by clinical specialists from 3D DE-MRI scans from Hospital Clinic de Barcelona patients. For each ground truth, 10 to 20 synthetic erroneous masks were created by adding common U-Net mistakes, such as common trunks between independent pulmonary veins (PVs) or holes and bumps in the LA surface and body. The final algorithm consisted of two different DAE models, one for bumps and holes removal and a another to separate mistakenly joined PVs, which increased the segmentations' mean dice between ROI and background classes up to 98.4% and 95.4%, respectively. Both numerical and visual results were analyzed. In conclusion, this work proves denoising autoencoders to be an effective post-processing step to refine the output of cardiac anatomical segmentations and improve the accuracy. Since the proposed approach is independent from the U-Net model, the results suggest that the it can lead to more reliable and efficient diagnoses in clinical applications that require accurate cardiac segmentations not only for LA but also for other anatomical regions