Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks

Towards Automatic Plan Selection for Radiotherapy of Cervical Cancer by Fast Automatic Segmentation of Cone Beam CT Scans

We propose a method to automatically select a treatment plan for radiotherapy of cervical cancer using a Plan-of-the-Day procedure, in which multiple treatment plans are constructed prior to treatment. The method comprises a multi-atlas based segmentation algorithm that uses the selected treatment plan to choose between two atlas sets. This segmentation only requires two registration procedures and can therefore be used in clinical practice without using excessive computation time. Our method is validated on a dataset of 224 treatment fractions for 10 patients. In 37 cases (16%), no recommendation was made by the algorithm due to poor image quality or registration results. In 93% of the remaining cases a correct recommendation for a treatment plan was given

NCBHR communiqué

International audienceThe combination of functional and anatomical imaging technologies such as Positron Emission Tomography (PET) and Computed Tomography (CT) has shown its value in the preclinical and clinical fields. In PET/CT hybrid acquisition systems, CT-derived attenuation maps enable a more accurate PET reconstruction. However, CT provides only very limited soft-tissue contrast and exposes the patient to an additional radiation dose. In comparison, Magnetic Resonance Imaging (MRI) provides good soft-tissue contrast and the ability to study functional activation and tissue microstructures, but does not directly provide patient-specific electron density maps for PET reconstruction.The aim of the proposed work is to improve PET/MR reconstruction by generating synthetic CTs and attenuation-maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient’s morphology to a database of pre-acquired MRI/CT pairs. Results show improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an Ultrashort-Echo-Time MRI sequence

Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation

International audienceQuantitative magnetic resonance analysis often requires accurate, robust and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert segmentation priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. During our experiments, the hippocampi of 80 healthy subjects were segmented. The influence on segmentation accuracy of different parameters such as patch size or number of training subjects was also studied. Moreover, a comparison with an appearance-based method and a template-based method was carried out. The highest median kappa value obtained with the proposed method was 0.884, which is competitive compared with recently published methods