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Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ's visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U2Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U2-Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound nerve segmentation dataset, U2-Net (U-Squared-Net) model achieved the best HD results (HC18: 3.8, CUM Dataset: 4, B-mode US Dataset: 3.6) and the lowest MAD values (HC18: 2.1, CUM Dataset: 3, B-mode US Dataset: 2.15). In addition, the analysis also highlights the architectural differences between these models, focusing on their type of connections, number of layers, and additional components. The outcome of this research provides valuable perception into the strengths and limitations of each encoder-decoder-based model, aiding researchers and practitioners in selecting the most appropriate model for Ultrasound image segmentation tasks. [ABSTRACT FROM AUTHOR]

Accurately segmenting the structure of the fetal head (FH) and performing biometry measurements, including head circumference (HC) estimation, stands as a vital requirement for addressing abnormal fetal growth during pregnancy under the expertise of experienced radiologists using ultrasound (US) images. However, accurate segmentation and measurement is a challenging task due to image artifact, incomplete ellipse fitting, and fluctuations due to FH dimensions over different trimesters. Also, it is highly time-consuming due to the absence of specialized features, which leads to low segmentation accuracy. To address these challenging tasks, we propose an automatic density regression approach to incorporate appearance and shape priors into the deep learning-based network model (DR-ASPnet) with robust ellipse fitting using fetal US images. Initially, we employed multiple pre-processing steps to remove unwanted distortions, variable fluctuations, and a clear view of significant features from the US images. Then some form of augmentation operation is applied to increase the diversity of the dataset. Next, we proposed the hierarchical density regression deep convolutional neural network (HDR-DCNN) model, which involves three network models to determine the complex location of FH for accurate segmentation during the training and testing processes. Then, we used post-processing operations using contrast enhancement filtering with a morphological operation model to smooth the region and remove unnecessary artifacts from the segmentation results. After post-processing, we applied the smoothed segmented result to the robust ellipse fitting-based least square (REFLS) method for HC estimation. Experimental results of the DR-ASPnet model obtain 98.86% dice similarity coefficient (DSC) as segmentation accuracy, and it also obtains 1.67 mm absolute distance (AD) as measurement accuracy compared to other state-of-the-art methods. Finally, we achieved a 0.99 correlation coefficient (CC) in estimating the measured and predicted HC values on the HC18 dataset. [ABSTRACT FROM AUTHOR]

Objective: Genetic diseases are an important cause of neonatal and childhood mortality. For couples with a history of demise of previous children, screening for carrier status can be done by exome sequencing (ES) of the parents. Our aim was to describe the clinical utility of "targeted parental ES" in such couples and to assess the utility of reanalysis of parental ES data. Method: We analyzed previous records, including ES reports, of 52 families with demise of previous offspring with a suspected genetic disorder. We also retrieved and reanalyzed raw data of parental ES in FASTQ format from the testing lab. Results: A potential diagnosis was obtained in 30/52 (57.7%) of couples. We found 38/70 (54.3%) novel variants in this cohort. Shared carrier status for more than one autosomal or X‐linked recessive disorder was identified in 18% of couples. Reanalysis of raw data resulted in a reclassification of variants in 15% of cases. Conclusion: Targeted parental ES can be helpful for families with demise of previous offspring with a suspected genetic disorder. Key points: What's already known about the topic? Many genetic disorders can be rapidly identified by exome sequencing (ES), but this is not always done early in the course of illness due to cost constraints, and many children succumb to the illness before a molecular diagnosis is made.ES of parents in such cases has a reported diagnostic yield of 38%–64%. What does this study add? Parental ES had a diagnostic yield of 57.7%, supporting its usefulness for the evaluation of couples with a history of previous loss of offsprings due to a suspected genetic disorder.Shared carrier status for more than one autosomal or X‐linked recessive disorder was found in 18%, suggesting this should be considered with parental ES.Reanalysis of parental exome data resulted in reclassification of variants in ∼15%. [ABSTRACT FROM AUTHOR]

Neurodegeneration with brain iron accumulation (NBIA) is an umbrella term encompassing various inherited neurological disorders characterised by abnormal iron accumulation in basal ganglia. We aimed to study the clinical, radiological and molecular spectrum of disorders with NBIA. All molecular-proven cases of NBIA presented in the last 5 years at 2 tertiary care genetic centres were compiled. Demographic details and clinical and neuroimaging findings were collated. We describe 27 individuals from 20 unrelated Indian families with causative variants in 5 NBIA-associated genes. PLA2G6-associated neurodegeneration (PLAN) was the most common, observed in 13 individuals from 9 families. They mainly presented in infancy with neuroregression and hypotonia. A recurrent pathogenic variant in COASY was observed in two neonates with prenatal-onset severe neurodegeneration. Pathogenic bi-allelic variants in PANK2, FA2H and C19ORF12 genes were observed in the rest, and these individuals presented in late childhood and adolescence with gait abnormalities and extrapyramidal symptoms. No intrafamilial and interfamilial variability were observed. Iron deposition on neuroimaging was seen in only 6/17 (35.3%) patients. A total of 22 causative variants across 5 genes were detected including a multiexonic duplication in PLA2G6. The variants c.1799G > A and c.2370 T > G in PLA2G6 were observed in three unrelated families. In silico assessments of 8 amongst 9 novel variants were also performed. We present a comprehensive compilation of the phenotypic and genotypic spectrum of various subtypes of NBIA from the Indian subcontinent. Clinical presentation of NBIAs is varied and not restricted to extrapyramidal symptoms or iron accumulation on neuroimaging. [ABSTRACT FROM AUTHOR]

Background: Antenatally detected occipital encephalocele and polycystic kidneys are a common presentation of ciliopathies like Joubert syndrome and Meckel Gruber syndrome which have considerable genetic and phenotypic overlap. Case reports: We describe 3 cases of antenatally diagnosed occipital encephalocele and enlarged kidneys with fetal autopsy, histopathology & exome sequencing results. A novel nonsense variant in the CEP290 gene was reported in first case (Meckel syndrome). The second case shows the importance of fetal exome where the parents were carriers for 2 ciliopathy genes (TMEM138 & SDCCAG8). Diagnosis in this case was confirmed by fetal exome sequencing (Joubert syndrome). Multiexon deletion in TMEM67 and KIF14 present in trans was identified in the third case (Meckel syndrome), likely resulting in digenic inheritance. Conclusion: We report 2 cases of Meckel syndrome with a novel variant and multiexon deletion, and 1 case of Joubert syndrome which depicts the limitations of preconceptional carrier screening in ciliopathies due to overlapping phenotypes. [ABSTRACT FROM AUTHOR]

Epidermolysis bullosa (EB) is a group of rare genodermatoses that is characterized by skin fragility resulting from minor trauma. There are four major subtypes, namely, EB simplex, junctional EB, dystrophic EB and Kindler EB, depending upon the localization of defective protein and resulting plane of blister formation. The phenotype is heterogeneous in terms of severity and majority of them present at birth or neonatal period. Currently, the treatment is mainly supportive and requires multidisciplinary care. The complex molecular pathology creates difficulty in discovering a unified curative treatment approach. But with arduous efforts, significant progress has been made in the development of treatment strategies in the last decade. The management strategies range from targeting the underlying causative factor to symptom-relieving approaches, and include gene, mRNA, protein, cell and combination therapies. In this review, we enumerate the promising approaches that are currently under various stages of investigation to provide effective treatment for patients with EB. [ABSTRACT FROM AUTHOR]