NDLI: Statistical Learning Algorithm for In-situ and Invasive Breast Carcinoma Segmentation

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Statistical Learning Algorithm for In-situ and Invasive Breast Carcinoma Segmentation

Content Provider	PubMed Central
Author	Jayender, Jagadeesan Gombos, Eva Sona, Chikarmane Dabydeen, Donnette Jolesz, Ferenc A. Vosburgh, Kirby G.
Copyright Year	2013
Abstract	DCE-MRI has proven to be a highly sensitive imaging modality in diagnosing breast cancers. However, analyzing the DCE-MRI is time-consuming and prone to errors due to the large volume of data. Mathematical models to quantify contrast perfusion, such as the Black Box methods and Pharmacokinetic analysis, are inaccurate, sensitive to noise and depend on a large number of external factors such as imaging parameters, patient physiology, arterial input function, fitting algorithms etc., leading to inaccurate diagnosis. In this paper, we have developed a novel Statistical Learning Algorithm for Tumor Segmentation (SLATS) based on Hidden Markov Models to auto-segment regions of angiogenesis, corresponding to tumor. The SLATS algorithm has been trained to identify voxels belonging to the tumor class using the time-intensity curve, first and second derivatives of the intensity curves (“velocity” and “acceleration” respectively) and a composite vector consisting of a concatenation of the intensity, velocity and acceleration vectors. The results of SLATS trained for the four vectors has been shown for 22 Invasive Ductal Carcinoma (IDC) and 19 Ductal Carcinoma In Situ (DCIS) cases. The SLATS trained for the velocity tuple shows the best performance in delineating the tumors when compared with the segmentation performed by an expert radiologist and the output of a commercially available software, CADstream.
Related Links	http://dx.doi.org/10.1016/j.compmedimag.2013.04.003
Ending Page	292
Page Count	12
Starting Page	281
File Format	PDF
ISSN	08956111
e-ISSN	18790771
Journal	Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Issue Number	4
Volume Number	37
Language	English
Publisher Date	2013-06-01
Access Restriction	Open
Subject Keyword	Radiological and Ultrasound Technology Health Informatics Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Computer Graphics and Computer-Aided Design Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology

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