scholarly journals Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma

2020 ◽  
Vol 2 (5) ◽  
pp. e200016 ◽  
Author(s):  
Amy J. Weisman ◽  
Minnie W. Kieler ◽  
Scott B. Perlman ◽  
Martin Hutchings ◽  
Robert Jeraj ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lymph Node ◽  
Convolutional Neural Networks ◽  
Pet Ct

scholarly journals Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset

2018 ◽  
Vol 31 (6) ◽  
pp. 851-856 ◽  
Author(s):  
Richard Ha ◽  
Peter Chang ◽  
Jenika Karcich ◽  
Simukayi Mutasa ◽  
Reza Fardanesh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lymph Node ◽  
Convolutional Neural Networks ◽  
Axillary Lymph Node ◽  
Axillary Lymph ◽  
Lymph Node Evaluation

scholarly journals 18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks

Radiology ◽  
2020 ◽  
Vol 294 (2) ◽  
pp. 445-452 ◽  
Author(s):  
Ludovic Sibille ◽  
Robert Seifert ◽  
Nemanja Avramovic ◽  
Thomas Vehren ◽  
Bruce Spottiswoode ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Deep Convolutional Neural Networks ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Segmentation of Lymph Nodes in Ultrasound Images using U-Net Convolutional Neural Networks and Gabor-based Anisotropic Diffusion

2021 ◽  
Author(s):  
Haobo Chen ◽  
Yuqun Wang ◽  
Jie Shi ◽  
Jingyu Xiong ◽  
Jianwei Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Convolutional Neural Networks ◽  
Anisotropic Diffusion ◽  
Speckle Noise ◽  
Accurate Method ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
Sensitivity Specificity

Abstract Objective Automated segmentation of lymph nodes (LNs) in ultrasound images is a challenging task mainly due to the presence of speckle noise and echogenic hila. In this paper, we propose a fully automatic and accurate method for LN segmentation in ultrasound. Methods The proposed segmentation method integrates diffusion-based despeckling, U-Net convolutional neural networks and morphological operations. Firstly, we suppress speckle noise and enhance lymph node edges using the Gabor-based anisotropic diffusion (GAD). Secondly, a modified U-Net model is proposed to segment LNs excluding echogenic hila. Finally, morphological operations are adopted to segment entire LNs by filling the regions of echogenic hila.Results A total of 531 lymph nodes from 526 patients were included to evaluate the proposed method. Quantitative metrics of segmentation performance, including the accuracy, sensitivity, specificity, Jaccard similarity and Dice coefficient, reached 0.934, 0.939, 0.937, 0.763 and 0.865, respectively.Conclusion The proposed method automatically and accurately segments LNs in ultrasound, which may assist artificially intelligent diagnosis of lymph node diseases.

scholarly journals Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks

2020 ◽  
Vol 33 (4) ◽  
pp. 888-894 ◽  
Author(s):  
Skander Jemaa ◽  
Jill Fredrickson ◽  
Richard A. D. Carano ◽  
Tina Nielsen ◽  
Alex de Crespigny ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Tumor Burden ◽  
Whole Body ◽  
Ct Scans ◽  
Tumor Segmentation ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
2D And 3D

Abstract 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) is commonly used in clinical practice and clinical drug development to identify and quantify metabolically active tumors. Manual or computer-assisted tumor segmentation in FDG-PET images is a common way to assess tumor burden, such approaches are both labor intensive and may suffer from high inter-reader variability. We propose an end-to-end method leveraging 2D and 3D convolutional neural networks to rapidly identify and segment tumors and to extract metabolic information in eyes to thighs (whole body) FDG-PET/CT scans. The developed architecture is computationally efficient and devised to accommodate the size of whole-body scans, the extreme imbalance between tumor burden and the volume of healthy tissue, and the heterogeneous nature of the input images. Our dataset consists of a total of 3664 eyes to thighs FDG-PET/CT scans, from multi-site clinical trials in patients with non-Hodgkin’s lymphoma (NHL) and advanced non-small cell lung cancer (NSCLC). Tumors were segmented and reviewed by board-certified radiologists. We report a mean 3D Dice score of 88.6% on an NHL hold-out set of 1124 scans and a 93% sensitivity on 274 NSCLC hold-out scans. The method is a potential tool for radiologists to rapidly assess eyes to thighs FDG-avid tumor burden.

Sign in / Sign up

Export Citation Format

Share Document