Statistical Learning Algorithm for in situ and invasive breast carcinoma segmentation.

Dynamic Contrast Enhanced MRI (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, and fitting algorithms, 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.