A novel automatic segmentation workflow of axial breast DCE-MRI

2018 ◽  
Author(s):  
Feten Besbes ◽  
Dorra Sellami ◽  
Norhene Gargouri ◽  
Alima Damak
Keyword(s):  
Automatic Segmentation ◽  
Dce Mri

scholarly journals Correction to: Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI

2019 ◽  
Vol 33 (2) ◽  
pp. 329-330
Author(s):  
Maren Marie Sjaastad Andreassen ◽  
Pål Erik Goa ◽  
Torill Eidhammer Sjøbakk ◽  
Roja Hedayati ◽  
Hans Petter Eikesdal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Response ◽  
Fdg Pet ◽  
Automatic Segmentation ◽  
Response Assessment ◽  
Original Version ◽  
Dce Mri ◽  
Breast Cancer Cohort ◽  
Treatment Response Assessment ◽  
18F Fdg

The original version of this article unfortunately contained a mistake in Fig. 6.

scholarly journals Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

2017 ◽  
Vol 23 (2) ◽  
pp. 29-36 ◽  
Author(s):  
Sathya D. Janaki ◽  
K. Geetha
Keyword(s):  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Random Search ◽  
Automatic Segmentation ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Dce Mri ◽  
Suspicious Region ◽  
Artificial Fish Swarm ◽  
Suspicious Lesions

Abstract Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.

scholarly journals Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Harini Veeraraghavan ◽  
Brittany Z. Dashevsky ◽  
Natsuko Onishi ◽  
Meredith Sadinski ◽  
Elizabeth Morris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feasibility Study ◽  
Automatic Segmentation ◽  
Dce Mri

Prediction of lymph node metastase in NSCLC

2010 ◽  
Vol 01 (05) ◽  
pp. 219-226 ◽  
Author(s):  
F. Beyer ◽  
B. Buerke ◽  
J. Gerss ◽  
K. Scheffe ◽  
M. Puesken ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Fdg Pet ◽  
Automatic Segmentation ◽  
Correlation Coefficients ◽  
Mediastinal Lymph Nodes ◽  
Intra Class Correlation ◽  
Axial Diameter ◽  
Pet Ct ◽  
Fdg Pet Ct

SummaryPurpose: To distinguish between benign and malignant mediastinal lymph nodes in patients with NSCLC by comparing 2D and semiautomated 3D measurements in FDG-PET-CT.Patients, material, methods: FDG-PET-CT was performed in 46 patients prior to therapy. 299 mediastinal lymph-nodes were evaluated independently by two radiologists, both manually and by semi-automatic segmentation software. Longest-axial-diameter (LAD), shortest-axial-diameter (SAD), maximal-3D-diameter, elongation and volume were obtained. FDG-PET-CT and clinical/FDG-PET-CT follow up examinations and/or histology served as the reference standard. Statistical analysis encompassed intra-class-correlation-coefficients and receiver-operator-characteristics-curves (ROC). Results: The standard of reference revealed involvement in 87 (29%) of 299 lymph nodes. Manually and semi-automatically measured 2D parameters (LAD and SAD) showed a good correlation with mean

Assoziationen zwischen der DCE-MRI und der Gefäßdichte bei Plattenepithelkarzinomen der Kopf-Hals Region.

2020 ◽  
Author(s):  
H Meyer ◽  
G Hamerla ◽  
L Leifels ◽  
A Höhn ◽  
A Surov
Keyword(s):  
Dce Mri

Comparative Analysis and Automatic Segmentation of PCG Signal Using Wavelets Transform

2015 ◽  
Vol 3 (3) ◽  
pp. 24-29
Author(s):  
Lekram Premlal Bahekar ◽  
◽  
Deepali Shende ◽  
Simran Kaur Digwa ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Automatic Segmentation ◽  
Wavelets Transform
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