Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhanced MRI using time series analysis

PURPOSE

To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast-enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling.

MATERIALS AND METHODS

Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise, and fitting algorithms. We modeled the underlying dynamics of the tumor by an LDS and used the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist's segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC).

RESULTS

The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared with the radiologist's segmentation and 82.1% accuracy and 100% sensitivity when compared with the CADstream output. The overlap of the algorithm output with the radiologist's segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72, respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC = 0.95.

CONCLUSION

The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI.

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.