Construction of Indian human brain atlas.

CONTEXT: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroimage analysis tasks as it provides an atlas with a standard coordinate system which is needed for spatial normalization of a brain MRI. Ideally, this atlas should be as near to the average brain of the population being studied as possible. AIMS: The aim of this study is to construct and validate the Indian brain MRI atlas of young Indian population and the corresponding structure probability maps. SETTINGS AND DESIGN: This was a population-specific atlas generation and validation process. MATERIALS AND METHODS: 100 young healthy adults (M/F = 50/50), aged 21-30 years, were recruited for the study. Three different 1.5-T scanners were used for image acquisition. The atlas and structure maps were created using nonrigid groupwise registration and label-transfer techniques. COMPARISON AND VALIDATION: The generated atlas was compared against other atlases to study the population-specific trends. RESULTS: The atlas-based comparison indicated a signifi cant difference between the global size of Indian and Caucasian brains. This difference was noteworthy for all three global measures, namely, length, width, and height. Such a comparison with the Chinese and Korean brain templates indicate all 3 to be comparable in length but signifi cantly different (smaller) in terms of height and width. CONCLUSIONS: The findings confirm that there is significant difference in brain morphology between Indian, Chinese, and Caucasian populations.

Evaluation of Peri-Implant Parameters and C-Reactive Protein Levels among Patients with Different Obesity Levels.

Background: Assessment of correlation between peri-implant parameters and C-reactive protein levels among patients with different obesity levels. Materials and Methods: Evaluation of 60 subjects was performed who were scheduled to undergo dental implant therapy for missing mandibular first molars. Three study cohorts were formed, namely, Group A: obese group (BMI between 30 Kg/m2 and 34.9 Kg/m2), Group B: high obese group (BMI over 34.9 Kg/m2), and Group C: non-obese group (BMI under 25 Kg/m2). Each cohort comprised 20 subjects. Dental implant therapy was carried out in all the patients. Peri-implant variables were evaluated in all the patients. Blood samples were obtained, and C-reactive protein levels in subjects having different obesity levels. Statistical analysis was performed using SPSS software. Results: Mean serum C-reactive protein levels among patients of groups A, B, and C occurred to be 3.28 mg/L, 3.65 g/L, and 3.61 g/L, respectively. On comparing numerically, noticeable outcomes were achieved. Mean probing depth among subjects of groups A, B, and C occurred to be 2.9 mm, 3.2 mm, and 1.3 mm, respectively. Mean marginal bone loss among subjects of groups A, B, and C occurred to be 2.1 mm, 2.7 mm, and 0.8 mm, respectively. On comparing numerically, noteworthy outcomes were gathered. Conclusion: There were significantly higher deranged peri-implant inflammatory variables among patients with higher levels of obesity.

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g. registration, segmentation) generally leads to an accumulation of errors that impacts the accuracy of downstream inference tasks. In this paper, we propose that by embedding uncertainty estimates across cascaded inference tasks, performance on the downstream inference tasks should be improved. We demonstrate the effectiveness of the proposed approach in three different clinical contexts: (i) We demonstrate that by propagating T2 weighted lesion segmentation results and their associated uncertainties, subsequent T2 lesion detection performance is improved when evaluated on a proprietary large-scale, multi-site, clinical trial dataset acquired from patients with Multiple Sclerosis. (ii) We show an improvement in brain tumour segmentation performance when the uncertainty map associated with a synthesised missing MR volume is provided as an additional input to a follow-up brain tumour segmentation network, when evaluated on the publicly available BraTS-2018 dataset. (iii) We show that by propagating uncertainties from a voxel-level hippocampus segmentation task, the subsequent regression of the Alzheimer's disease clinical score is improved.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

Cochlear Implantation in Pierre Robin Syndrome.

Hereditary hearing loss accounts for nearly 60% of deafness in developed countries and about 30% of them are syndromic. Pierre Robin Syndrome is one such condition. The patient with this syndrome usually presnts with triad of micrognathia, glossoptosis and cleft palate. Hearing loss is mostly conductive but there can be sensorineural hearing loss also. Here we present a case of Pierre Robin Syndrome who presented with congenital hearing loss. He also had bilateral serous otitis media. He underwent cochlear implant surgery and was prescribed antihistaminics and steroid spray for middle ear effusion. Therefore, proper clinical evaluation is required.

BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures.

Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.