5 research outputs found
Identification of Hemorrhage and Infarct Lesions on Brain CT Images using Deep Learning
Head Non-contrast computed tomography (NCCT) scan remain the preferred
primary imaging modality due to their widespread availability and speed.
However, the current standard for manual annotations of abnormal brain tissue
on head NCCT scans involves significant disadvantages like lack of cutoff
standardization and degeneration identification. The recent advancement of deep
learning-based computer-aided diagnostic (CAD) models in the multidisciplinary
domain has created vast opportunities in neurological medical imaging.
Significant literature has been published earlier in the automated
identification of brain tissue on different imaging modalities. However,
determining Intracranial hemorrhage (ICH) and infarct can be challenging due to
image texture, volume size, and scan quality variability. This retrospective
validation study evaluated a DL-based algorithm identifying ICH and infarct
from head-NCCT scans. The head-NCCT scans dataset was collected consecutively
from multiple diagnostic imaging centers across India. The study exhibits the
potential and limitations of such DL-based software for introduction in routine
workflow in extensive healthcare facilities
3DCoMPaT: An improved Large-scale 3D Vision Dataset for Compositional Recognition
In this work, we present 3DCoMPaT, a multimodal 2D/3D dataset with 160
million rendered views of more than 10 million stylized 3D shapes carefully
annotated at the part-instance level, alongside matching RGB point clouds, 3D
textured meshes, depth maps, and segmentation masks. 3DCoMPaT covers 41
shape categories, 275 fine-grained part categories, and 293 fine-grained
material classes that can be compositionally applied to parts of 3D objects. We
render a subset of one million stylized shapes from four equally spaced views
as well as four randomized views, leading to a total of 160 million renderings.
Parts are segmented at the instance level, with coarse-grained and fine-grained
semantic levels. We introduce a new task, called Grounded CoMPaT Recognition
(GCR), to collectively recognize and ground compositions of materials on parts
of 3D objects. Additionally, we report the outcomes of a data challenge
organized at CVPR2023, showcasing the winning method's utilization of a
modified PointNet model trained on 6D inputs, and exploring alternative
techniques for GCR enhancement. We hope our work will help ease future research
on compositional 3D Vision.Comment: https://3dcompat-dataset.org/v2
Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement
A stroke occurs when an artery in the brain ruptures and bleeds or when the
blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's
tissues due to the rupture or obstruction resulting in tissue death. The Middle
cerebral artery (MCA) is the largest cerebral artery and the most commonly
damaged vessel in stroke. The quick onset of a focused neurological deficit
caused by interruption of blood flow in the territory supplied by the MCA is
known as an MCA stroke. Alberta stroke programme early CT score (ASPECTS) is
used to estimate the extent of early ischemic changes in patients with MCA
stroke. This study proposes a deep learning-based method to score the CT scan
for ASPECTS. Our work has three highlights. First, we propose a novel method
for medical image segmentation for stroke detection. Second, we show the
effectiveness of AI solution for fully-automated ASPECT scoring with reduced
diagnosis time for a given non-contrast CT (NCCT) Scan. Our algorithms show a
dice similarity coefficient of 0.64 for the MCA anatomy segmentation and 0.72
for the infarcts segmentation. Lastly, we show that our model's performance is
inline with inter-reader variability between radiologists