☆ 4.5 Article

Segmentation of Brain Metastases Using Background Layer Statistics (BLAST)

AMERICAN JOURNAL OF NEURORADIOLOGY (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Oncology

Automated Brain Metastases Segmentation With a Deep Dive Into False-positive Detection

Hamidreza Ziyaee et al.

Summary: An automated framework for contouring brain metastases in MRI was developed to assist treatment planning for stereotactic radiosurgery (SRS). The performance of the framework was evaluated and showed high accuracy and clinical acceptability. The tool can help radiologists and radiation oncologists detect and contour tumors from MRI, as well as potentially identify lesions at early stages.

ADVANCES IN RADIATION ONCOLOGY (2023)

添加到收藏夹

Article Oncology

Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery

Jen-Yeu Wang et al.

Summary: This study evaluated the performance of the VBrain deep learning algorithm in detecting and segmenting brain metastases. Results showed that the software accurately detected brain metastases with 96.23% sensitivity for lesions with diameters of 5 mm or higher.

RADIATION ONCOLOGY (2023)

添加到收藏夹

Review Medicine, General & Internal

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Hana Baroudi et al.

Summary: This paper discusses various aspects of assessing the clinical acceptability of AI-based tools for contouring and treatment planning in radiotherapy, and explores how to establish a standard for defining the clinical acceptability of new autocontouring and planning tools.

DIAGNOSTICS (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

Andreas M. Rauschecker et al.

Summary: This study assessed the performance of a brain MRI lesion segmentation algorithm trained at one institution and applied at another institution. It also explored the impact of multi-institutional training datasets on performance loss mitigation. The results showed that adding relevant training data from an external institution supported the successful application of the previously trained model to the external institution.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2022)

添加到收藏夹

Article Oncology

Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Shao-Lun Lu et al.

Summary: The study found that in brain tumor SRS, AI-assisted contouring significantly improved inter-reader agreement and lesion detection sensitivity, especially for less experienced physicians. AI assistance not only increased contouring accuracy, but also improved work efficiency, with a notable impact on time-saving.

NEURO-ONCOLOGY (2021)

添加到收藏夹

Article Engineering, Biomedical

Automatic segmentation of brain metastases using T1 magnetic resonance and computed tomography images

Dylan G. Hsu et al.

Summary: By utilizing a deep learning approach with co-registered MR and CT images, we developed a model that automatically detects and segments brain metastases. The model showed high sensitivity and accuracy in identifying metastases, demonstrating promising potential for clinical applications.

PHYSICS IN MEDICINE AND BIOLOGY (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases

Jeffrey D. Rudie et al.

Summary: A neural network was developed and validated for automated detection and segmentation of intracranial metastases, showing high accuracy in segmenting brain metastases.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multisequence MRI

Endre Grovik et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2020)

添加到收藏夹

Article Oncology

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Khaled Bousabarah et al.

RADIATION ONCOLOGY (2020)

添加到收藏夹

Article Clinical Neurology

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field

O. Shearkhani et al.

AMERICAN JOURNAL OF NEURORADIOLOGY (2017)

添加到收藏夹

Article Medicine, General & Internal

Effect of Radiosurgery Alone vs Radiosurgery With Whole Brain Radiation Therapy on Cognitive Function in Patients With 1 to 3 Brain Metastases A Randomized Clinical Trial

Paul D. Brown et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)

添加到收藏夹

Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Importance of Contrast-Enhanced Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging in Various Intracranial Pathologic Conditions

Eun Kyoung Lee et al.

KOREAN JOURNAL OF RADIOLOGY (2016)

添加到收藏夹

Review Mathematical & Computational Biology

MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

Ivana Despotovic et al.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2015)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

3D Slicer as an image computing platform for the Quantitative Imaging Network

Andriy Fedorov et al.

MAGNETIC RESONANCE IMAGING (2012)

添加到收藏夹

Review Medicine, General & Internal

Whole brain radiotherapy for the treatment of newly diagnosed multiple brain metastases

May N. Tsao et al.

COCHRANE DATABASE OF SYSTEMATIC REVIEWS (2012)

添加到收藏夹

Article Neurosciences

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

Mohamed L. Seghier et al.

NEUROIMAGE (2008)

添加到收藏夹

Article Neurosciences

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Paul A. Yushkevich et al.

NEUROIMAGE (2006)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.