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In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high- and low-grade. The proposed CNNs model is inspired by the Occipito-Temporal pathway which has a special function called selective attention that uses different receptive field sizes in successive layers to figure out the crucial objects in a scene. Thus, using selective attention technique to develop the CNNs model, helps to maximize the extraction of relevant features from MRI images. We have also addressed two more issues: class-imbalance, and the spatial relationship among image Patches. To address the first issue, we propose two steps: an equal sampling of images Patches and an experimental analysis of the effect of weighted cross-entropy loss function on the segmentation results. In addition, to overcome the second issue, we have studied the effect of Overlapping Patches against Adjacent Patches where the Overlapping Patches show better segmentation results due to the introduction of the global context as well as the local features of the image Patches compared to the conventionnel Adjacent Patches. Our experiment results are reported on BRATS-2018 dataset where our End-to-End Deep Learning model achieved state-of-the-art performance. The median Dice score of our fully automatic segmentation model is 0.90, 0.83, 0.83 for the whole tumor, tumor core, and enhancing tumor respectively compared to the Dice score of radiologist, that is in the range 74% - 85%. Moreover, our proposed CNNs model is not only computationally efficient at inference time, but it could segment the whole brain on average 12 seconds. Finally, the proposed Deep Learning model provides an accurate and reliable segmentation result, and that makes it suitable for adopting in research and as a part of different clinical settings.
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http://dx.doi.org/10.1016/j.media.2020.101692 | DOI Listing |
Neurooncol Adv
December 2024
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Background: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
School of Automation, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals.
View Article and Find Full Text PDFComput Biol Med
December 2024
Urology Division, Surgery Department, Sidra Medicine, Qatar; College of Medicine, Qatar University, Doha, Qatar; Weill Cornell Medicine Qatar, Doha, Qatar. Electronic address:
Introduction: Posterior Urethral Valves (PUV) are rare congenital anomalies of the male urinary tract that can lead to urethral obstruction and increased risk of kidney disease. Traditional diagnosis relies on subjective interpretation of imaging techniques. This study aimed to automate and increase accuracy of PUV detection in voiding cystourethrography (VCUG) images using an AI-based pipeline.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Hansung University, Seoul, Republic of Korea.
Background: As health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency. Among the forefront of these innovations are large language models (LLMs), a subset of artificial intelligence designed to understand, generate, and interact with human language at an unprecedented scale.
Objective: This systematic review describes the role of LLMs in improving diagnostic accuracy, automating documentation, and advancing specialist education and patient engagement within the field of gastroenterology and gastrointestinal endoscopy.
Sci Rep
December 2024
Department of Internal Medicine I, TUM University Hospital, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
In contemporary healthcare, effective risk stratification in the general population is vital amidst rising chronic disease rates and an ageing demographic. Deceleration Capacity of the heart rate (DC), derived from 24-hour Holter electrocardiograms, holds promise in risk stratification for cardiac patients. However, the potential of short-term electrocardiograms of five minutes duration for population screening has not been fully explored.
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