A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI

2011 ◽  
Author(s):  
A. Firjani ◽  
A. Elnakib ◽  
F. Khalifa ◽  
G. Gimel'farb ◽  
M. Abo El-Ghar ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Dce Mri ◽  
Segmentation Framework

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

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 Distinguishing Adenocarcinomas From Granulomas in The CT Scan of The Chest: Performance Degradation Evaluation in The Automatic Segmentation Framework

2021 ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
Ct Scan ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
The State ◽  
Ct Scans ◽  
Standards Of Care ◽  
Shape Features ◽  
Segmentation Framework ◽  
Degradation Evaluation

Abstract Objective: The most common histopathologic malignant and benign nod- ules are Adenocarcinoma and Granuloma, respectively, which have di_erent standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuos- ity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker pa- tients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results: We compare our framework with the state-of-the-art feature selec- tion methods for di_erentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

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