Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

Kumar, Puranam Revanth; Jha, Rajesh Kumar; Kumar, P Akhendra; Raju, B Deevena

doi:10.1016/j.imed.2023.10.001

Intelligent Medicine

2024

DOI: 10.1016/j.imed.2023.10.001

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Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

Puranam Revanth Kumar,

Rajesh Kumar Jha,

P Akhendra Kumar

et al.

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2024

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Cited by 2 publications

References 29 publications

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Genetic Heterogeneity in Four Probands Reveals HGSNAT, KDM6B, LMNA and WFS1 Related Neurodevelopmental Disorders

Mudassir,

Williams

et al. 2024

Biomedicines

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Background: Neurodevelopmental disorders of genetic etiology are a highly diverse set of congenital recurrent complications triggered by irregularities in the basic tenets of brain development. Methods: We present whole exome sequencing analysis and expression characteristics of the probands from four unrelated Pakistani consanguineous families with facial dysmorphism, neurodevelopmental, ophthalmic, auditory, verbal, psychiatric, behavioral, dental, and skeletal manifestations otherwise unexplained by clinical spectrum. Results: Whole exome sequencing identifies a novel, bi-allelic, missense variant in the HGSNAT gene [NM_152419.3: c.1411G > A (p. Glu471Lys) exon 14] for proband family E-1 and a rare, bi-allelic, non-frameshift variant in the KDM6B gene [NM_001348716.2: c.786_791dupACCACC (p. Pro263_Pro264dup) exon 10] for proband family E-2, and a novel, mono-allelic, missense variant in the LMNA gene [NM_170707.4: c. 1328 A > G (p. Glu443Gly) exon 8] for proband family E-3 and an ultra-rare, mono-allelic, missense variant in the WFS1 gene [NM_006005.3: c.2131G > A (p. Asp711Asn) exon 8] for proband family E-4. Protein modelling shows conformation and size modifications in mutated residues causing damage to the conserved domains expressed as neurocognitive pathology. Conclusions: The current study broadens the distinctly cultural and genetically inbred pool of the Pakistani population for harmful mutations, contributing to the ever-expanding phenotypic palette. The greatest aspirations are molecular genetic profiling and personalized treatment for individuals with complex neurological symptoms to improve their life activities.

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Genetic Heterogeneity in Four Probands Reveals HGSNAT, KDM6B, LMNA and WFS1 Related Neurodevelopmental Disorders

Mudassir,

Williams

et al. 2024

Biomedicines

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Advanced U-Net Model for Precise Brain Tumor Segmentation in MRI Images

2024

jdaai

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The accuracy of medical image segmentation is crucial for diagnosis and treatment planning in the modern healthcare system. Deep learning methods, like CNNs, UNETs, and Transformers, have completely changed this industry by automating labor-intensive manual segmentation procedures that were previously done by hand. However, problems like complex architectures and blurry characteristics continue, which causes issues with accuracy. Researchers are working hard to overcome these obstacles to fully realize the potential of medical image segmentation in the revolution of healthcare. Our paper presents an enhanced U-Net model specifically designed for brain tumour MRI image segmentation to improve precision. There are three primary components to our strategy. First, we prioritize feature augmentation using methods like CLAHE in the picture preprocessing phase. Second, we modify the U-Net model's architecture with an emphasis on a customized layered design in order to improve segmentation outcomes. Finally, we use a CNN model for post-processing to further optimize segmentation results using further convolutional layers. A total of 3,064 brain MRI pictures were used to test (612 images), validate (612 images), and train (1,840 images) our model. We obtained exceptional recall (93.66%), accuracy (97.79%), F-score (93.15%), and precision (92.66%). The Dice coefficient's training and validation curves showed little variation, with training reaching roughly 93% and validation 84%, suggesting good generalization ability. High accuracy was validated by visual review of the segmentation findings, albeit occasionally little mistakes like false positives were noticed.

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Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

Cited by 2 publications

References 29 publications

Genetic Heterogeneity in Four Probands Reveals HGSNAT, KDM6B, LMNA and WFS1 Related Neurodevelopmental Disorders

Genetic Heterogeneity in Four Probands Reveals HGSNAT, KDM6B, LMNA and WFS1 Related Neurodevelopmental Disorders

Advanced U-Net Model for Precise Brain Tumor Segmentation in MRI Images

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