Comparative Study of Deep Learning Models for Segmentation of Corpus Callosum

Shrivastava, Siddharth; Singh, Nitisha; Mishra, Upasana; Chandra, Anjali; Verma, Shrish

doi:10.1109/iccmc48092.2020.iccmc-00079

Cited by 8 publications

(1 citation statement)

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“…Alternatives to BET include standard template-based brain-extraction. 123 Deep learning-based segmentation, a rapidly developing field, [124][125][126][127] may also be considered.…”

Section: Overviewmentioning

confidence: 99%

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro‐magnetic tissue properties study group

Bilgic,

Costagli,

Chan

et al. 2024

Magnetic Resonance in Med

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This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro‐Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi‐echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi‐echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase‐quality‐based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity‐based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

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“…Alternatives to BET include standard template-based brain-extraction. 123 Deep learning-based segmentation, a rapidly developing field, [124][125][126][127] may also be considered.…”

Section: Overviewmentioning

confidence: 99%