Automated template-based brain localization and extraction for fetal brain MRI reconstruction.

Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks

Mitochondrial quality control and neurological disease: an emerging connection

The human brain is a highly complex organ with remarkable energy demands. Although it represents only 2% of the total body weight, it accounts for 20% of all oxygen consumption, reflecting its high rate of metabolic activity. Mitochondria have a crucial role in the supply of energy to the brain. Consequently, their deterioration can have important detrimental consequences on the function and plasticity of neurons, and is thought to have a pivotal role in ageing and in the pathogenesis of several neurological disorders. Owing to their inherent physiological functions, mitochondria are subjected to particularly high levels of stress and have evolved specific molecular quality-control mechanisms to maintain the mitochondrial components. Here, we review some of the most recent advances in the understanding of mitochondrial stress-control pathways, with a particular focus on how defects in such pathways might contribute to neurodegenerative disease

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

Automatic segmentation of the bones from MR images of the knee

We present and validate a hybrid segmentation scheme based around 3D active shape models, which is used to automatically segment the three bones in the knee joint. This scheme is automatically initialised using an affine registration to an atlas. The accuracy and robustness of the approach was experimentally validated using an MR database of 20 fat suppressed Spoiled Gradient Recall images. A median Dice Similarity Coefficient (DSC) of 0.89, 0.96 and 0.96 was obtained for the patella, tibia and femur which illustrates the accuracy of the approach. The robustness of this scheme to initialisation was validated by segmenting each knee image 19 times, each time using a different image in the database as the atlas. An overall segmentation failure rate (DSC < 0.75) of only 3.60% shows that the scheme was robust to initialisation