Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients.

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD).

No Structural Differences Are Revealed by VBM in 'De Novo' Parkinsonian Patients.

The identification of brain morphological alterations in newly diagnosed PD patients (i.e. 'de novo') could potentially serve as a biomarker and accelerate diagnosis.