5 research outputs found
nnDetection for Intracranial Aneurysms Detection and Localization
Intracranial aneurysms are a commonly occurring and life-threatening
condition, affecting approximately 3.2% of the general population.
Consequently, detecting these aneurysms plays a crucial role in their
management. Lesion detection involves the simultaneous localization and
categorization of abnormalities within medical images. In this study, we
employed the nnDetection framework, a self-configuring framework specifically
designed for 3D medical object detection, to detect and localize the 3D
coordinates of aneurysms effectively. To capture and extract diverse features
associated with aneurysms, we utilized TOF-MRA and structural MRI, both
obtained from the ADAM dataset. The performance of our proposed deep learning
model was assessed through the utilization of free-response receiver operative
characteristics for evaluation purposes. The model's weights and 3D prediction
of the bounding box of TOF-MRA are publicly available at
https://github.com/orouskhani/AneurysmDetection.Comment: 6 pages, 4 figure
EMCSO: An Elitist Multi-Objective Cat Swarm Optimization
This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses thenon-dominated sorting method to findthe solutionsas close as to POFand crowding distance technique toobtain a uniform distribution among thenon-dominated solutions. Also, the algorithm is allowedto keep the elites of population in reproduction processand use an opposition-based learning method for population initialization to enhance the convergence speed.The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based onperformance measures of generational distance (GD), inverted GD, spread,and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem
Artificial Intelligence in Fetal Resting-State Functional MRI Brain Segmentation: A Comparative Analysis of 3D UNet, VNet, and HighRes-Net Models
Introduction: Fetal resting-state functional magnetic resonance imaging
(rs-fMRI) is a rapidly evolving field that provides valuable insight into brain
development before birth. Accurate segmentation of the fetal brain from the
surrounding tissue in nonstationary 3D brain volumes poses a significant
challenge in this domain. Current available tools have 0.15 accuracy. Aim: This
study introduced a novel application of artificial intelligence (AI) for
automated brain segmentation in fetal brain fMRI, magnetic resonance imaging
(fMRI). Open datasets were employed to train AI models, assess their
performance, and analyze their capabilities and limitations in addressing the
specific challenges associated with fetal brain fMRI segmentation. Method: We
utilized an open-source fetal functional MRI (fMRI) dataset consisting of 160
cases (reference: fetal-fMRI - OpenNeuro). An AI model for fMRI segmentation
was developed using a 5-fold cross-validation methodology. Three AI models were
employed: 3D UNet, VNet, and HighResNet. Optuna, an automated
hyperparameter-tuning tool, was used to optimize these models. Results and
Discussion: The Dice scores of the three AI models (VNet, UNet, and
HighRes-net) were compared, including a comparison between manually tuned and
automatically tuned models using Optuna. Our findings shed light on the
performance of different AI models for fetal resting-state fMRI brain
segmentation. Although the VNet model showed promise in this application,
further investigation is required to fully explore the potential and
limitations of each model, including the HighRes-net model. This study serves
as a foundation for further extensive research into the applications of AI in
fetal brain fMRI segmentation