4 research outputs found
Brain Extraction comparing Segment Anything Model (SAM) and FSL Brain Extraction Tool
Brain extraction is a critical preprocessing step in almost every
neuroimaging study, enabling accurate segmentation and analysis of Magnetic
Resonance Imaging (MRI) data. FSL's Brain Extraction Tool (BET), although
considered the current gold standard, presents limitations such as
over-extraction, which can be particularly problematic in brains with lesions
affecting the outer regions, inaccurate differentiation between brain tissue
and surrounding meninges, and susceptibility to image quality issues. Recent
advances in computer vision research have led to the development of the Segment
Anything Model (SAM) by Meta AI, which has demonstrated remarkable potential
across a wide range of applications. In this paper, we present a comparative
analysis of brain extraction techniques using BET and SAM on a variety of brain
scans with varying image qualities, MRI sequences, and brain lesions affecting
different brain regions. We find that SAM outperforms BET based on several
metrics, particularly in cases where image quality is compromised by signal
inhomogeneities, non-isotropic voxel resolutions, or the presence of brain
lesions that are located near or involve the outer regions of the brain and the
meninges. These results suggest that SAM has the potential to emerge as a more
accurate and precise tool for a broad range of brain extraction applications.Comment: 9 pages, 4 figures, 2 tables, SI in the given ur
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data
Brain tumors, particularly glioblastoma, continue to challenge medical
diagnostics and treatments globally. This paper explores the application of
deep learning to multi-modality magnetic resonance imaging (MRI) data for
enhanced brain tumor segmentation precision in the Sub-Saharan Africa patient
population. We introduce an ensemble method that comprises eleven unique
variations based on three core architectures: UNet3D, ONet3D, SphereNet3D and
modified loss functions. The study emphasizes the need for both age- and
population-based segmentation models, to fully account for the complexities in
the brain. Our findings reveal that the ensemble approach, combining different
architectures, outperforms single models, leading to improved evaluation
metrics. Specifically, the results exhibit Dice scores of 0.82, 0.82, and 0.87
for enhancing tumor, tumor core, and whole tumor labels respectively. These
results underline the potential of tailored deep learning techniques in
precisely segmenting brain tumors and lay groundwork for future work to
fine-tune models and assess performance across different brain regions.Comment: 3 figs and 3 table
Automated Ensemble-Based Segmentation of Pediatric Brain Tumors: A Novel Approach Using the CBTN-CONNECT-ASNR-MICCAI BraTS-PEDs 2023 Challenge Data
Brain tumors remain a critical global health challenge, necessitating
advancements in diagnostic techniques and treatment methodologies. In response
to the growing need for age-specific segmentation models, particularly for
pediatric patients, this study explores the deployment of deep learning
techniques using magnetic resonance imaging (MRI) modalities. By introducing a
novel ensemble approach using ONet and modified versions of UNet, coupled with
innovative loss functions, this study achieves a precise segmentation model for
the BraTS-PEDs 2023 Challenge. Data augmentation, including both single and
composite transformations, ensures model robustness and accuracy across
different scanning protocols. The ensemble strategy, integrating the ONet and
UNet models, shows greater effectiveness in capturing specific features and
modeling diverse aspects of the MRI images which result in lesion_wise dice
scores of 0.52, 0.72 and 0.78 for enhancing tumor, tumor core and whole tumor
labels respectively. Visual comparisons further confirm the superiority of the
ensemble method in accurate tumor region coverage. The results indicate that
this advanced ensemble approach, building upon the unique strengths of
individual models, offers promising prospects for enhanced diagnostic accuracy
and effective treatment planning for brain tumors in pediatric brains.Comment: 3 Figs, 3 Table
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions
A major challenge in stroke research and stroke recovery predictions is the
determination of a stroke lesion's extent and its impact on relevant brain
systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR)
imaging volumes, the current gold standard, is not only very time-consuming,
but its accuracy highly depends on the operator's experience. As a result,
there is a need for a fully automated segmentation method that can efficiently
and objectively measure lesion extent and the impact of each lesion to predict
impairment and recovery potential which might be beneficial for clinical,
translational, and research settings. We have implemented and tested a fully
automatic method for stroke lesion segmentation which was developed using eight
different 2D-model architectures trained via transfer learning (TL) and mixed
data approaches. Additionally, the final prediction was made using a novel
ensemble method involving stacking and agreement window. Our novel method was
evaluated in a novel in-house dataset containing 22 T1w brain MR images, which
were challenging in various perspectives, but mostly because they included T1w
MR images from the subacute (which typically less well defined T1 lesions) and
chronic stroke phase (which typically means well defined T1-lesions).
Cross-validation results indicate that our new method can efficiently and
automatically segment lesions fast and with high accuracy compared to ground
truth. In addition to segmentation, we provide lesion volume and weighted
lesion load of relevant brain systems based on the lesions' overlap with a
canonical structural motor system that stretches from the cortical motor region
to the lowest end of the brain stem.Comment: 13 Pages, 5 figures, 3 table