This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis,...
OBJECTIVE: The internet, particularly social media, has become a popular resource for learning about health and investigating one's own health conditions. The development of artificial intelligence (AI) chatbots has been fueled by the increasing avai...
Journal of imaging informatics in medicine
May 9, 2024
Semantic segmentation of tumours plays a crucial role in fundamental medical image analysis and has a significant impact on cancer diagnosis and treatment planning. UNet and its variants have achieved state-of-the-art results on various 2D and 3D med...
OBJECTIVE: It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly ...
International journal of neural systems
Apr 30, 2024
Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. ...
OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differe...
BMC medical informatics and decision making
Apr 30, 2024
Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter ...
BACKGROUND: Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, m...
Journal of cancer research and clinical oncology
Apr 29, 2024
PURPOSE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-yea...
Journal of applied clinical medical physics
Apr 25, 2024
PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-...