A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …
segmentation with input prompts such as points and bounding boxes. Its success largely …
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans
is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores …
is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores …
A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation
M Nijiati, A Tuersun, Y Zhang, Q Yuan, P Gong… - Frontiers in …, 2022 - frontiersin.org
Background: Accurate localization and classification of intracerebral hemorrhage (ICH)
lesions are of great significance for the treatment and prognosis of patients with ICH. The …
lesions are of great significance for the treatment and prognosis of patients with ICH. The …
A deep learning model for automatic segmentation of intraparenchymal and intraventricular hemorrhage for catheter puncture path planning
G Tong, X Wang, H Jiang, A Wu… - IEEE journal of …, 2023 - ieeexplore.ieee.org
Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially
when it also causes secondary intraventricular hemorrhage. The optimal surgical option for …
when it also causes secondary intraventricular hemorrhage. The optimal surgical option for …
Automatic segmentation of intracranial hemorrhage in computed tomography scans with convolution neural networks
Purpose Intracranial hemorrhage (ICH) is a serious health problem requiring prompt and
intensive medical treatment. The delineation of hemorrhage areas and the estimation of …
intensive medical treatment. The delineation of hemorrhage areas and the estimation of …
Segmentation of Tiny Intracranial Hemorrhage Via Learning-to-Rank Local Feature Enhancement
Intracranial hemorrhage (ICH) is a common head disease that can result in significant
disability or mortality. Segmentation of ICH is an important yet challenging step for medical …
disability or mortality. Segmentation of ICH is an important yet challenging step for medical …
Knowledge-prompted intracranial hemorrhage segmentation on brain computed tomography
Intracranial hemorrhage poses a critical threat to patient survival, necessitating rapid
intervention to prevent devastating outcomes. Traditional segmentation methods in …
intervention to prevent devastating outcomes. Traditional segmentation methods in …
Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation
Y Zhang, Y Huang, K Hu - Neural Networks, 2024 - Elsevier
Segmentation and the subsequent quantitative assessment of the target object in computed
tomography (CT) images provide valuable information for the analysis of intracerebral …
tomography (CT) images provide valuable information for the analysis of intracerebral …
UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation
Organ segmentation has become a preliminary task for computer-aided intervention,
diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from …
diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from …