Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning-Reference-Cited by-同舟云学术

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

Published:2020-07-01 Issue:4 Volume:2 Page:e190102
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Humpire-Mamani Gabriel E.¹^ORCID,Bukala Joris¹^ORCID,Scholten Ernst T.¹^ORCID,Prokop Mathias¹^ORCID,van Ginneken Bram¹^ORCID,Jacobs Colin¹^ORCID

Affiliation:

1. From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10 (Route 767), 6525 GA, Nijmegen, the Netherlands (G.E.H.M., J.B., E.T.S., M.P., B.v.G., C.J.); and Fraunhofer MEVIS, Bremen, Germany (B.v.G.).

Funder

Radboud University

Publisher

Radiological Society of North America (RSNA)

Subject

Artificial Intelligence,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2020190102

Reference36 articles.

1. Splenic volume measurements on computed tomography utilizing automatically contouring software and its relationship with age, gender, and anthropometric parameters

2. Radiology of the spleen

3. The Anatomy of the Spleen

4. Effect of selective internal radiation therapy and hepatic arterial chemotherapy on normal liver volume and spleen volume

5. Changes in spleen volume after resection of hepatic colorectal metastases

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Diagnostic ability of [18F]FDG PET/CT for distinguishing benign from malignant spleen lesions;European Radiology;2024-07-18

2. Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets;American Journal of Roentgenology;2024-07

3. Integrating YOLO and 3D U-Net for COVID-19 Diagnosis on Chest CT Scans;2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS);2024-06-26

4. Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images;Biomedical Physics & Engineering Express;2024-01-05

5. Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT;Diagnostics;2023-12-08