In this paper, we present ClinicaDL, an open-source software platform that aims at enhancing the reproducibility and rigor of research for deep learning in neuroimaging. We first provide an overview of the software platform and then focus on recent advances. Features of the software aim at addressing three key issues in the field: the lack of reproducibility, the methodological flaws that plague many published studies and the difficulties using neuroimaging datasets for people with little expertise in this application area. Key existing functionalities include automatic data splitting, checking for data leakage, standards for data organization and results storing, continuous integration and integration with Clinica for preprocessing, amongst others. The most prominent recent features are as follows. We now provide various data augmentation and synthetic data generation functions (both standard and advanced ones including motion and hypometabolism simulation). Continuous integration test data are now versioned using DVC (data version control). Tools for generating validation splits have been made more generic. We made major improvements regarding usability and performance. We now support multi-GPU training and automatic mixed precision (to exploit tensor cores). We created a graphical interface to easily generate training specifications. We allow tracking of experiments through standard tools (MLflow, Weights&Biases). We believe that ClinicaDL can contribute to enhance the trustworthiness of research in deep learning for neuroimaging. Moreover, its functionalities and coding practices may serve as inspiration for the whole medical imaging community, beyond neuroimaging.
Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows identifying a wide variety of anomalies from unlabelled data. It relies on reconstructing a subject-specific model of healthy appearance to which a subject’s image can be compared to detect anomalies. In the literature, it is common for anomaly detection to rely on analysing the residual image between the subject’s real image and its pseudo-healthy reconstruction. This approach however has limitations partly due to the pseudo-healthy reconstructions being imperfect and to the lack of natural thresholding mechanism. Our proposed method, inspired by Z-scores, leverages the healthy population variability to overcome these limitations. Our experiments conducted on 3D FDG PET scans from the ADNI database demonstrate the effectiveness of our approach in accurately identifying simulated Alzheimer’s disease related anomalies.
Unsupervised anomaly detection using deep learning models is a popular computer-aided diagnosis approach because it does not need annotated data and is not restricted to the diagnosis of a disease seen during training. Such approach consists in first learning the distribution of anomaly free images. Images presenting anomalies are then detected as outliers of this distribution. These approaches have been widely applied in neuroimaging to detect sharp and localized anomalies such as tumors or white matter hyper-intensities from structural MRI. In this work, we aim to detect anomalies from FDG PET images of patients with Alzheimer’s disease. In this context, the anomalies can be subtle and difficult to delineate, making the task more difficult and meaning that no ground truth exists to evaluate the approaches. We thus propose a framework to evaluate unsupervised anomaly detection approaches that consists in simulating realistic anomalies from images of healthy subjects. We demonstrate the use of this framework by evaluating an approach based on a 3D variational autoencoder.
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