Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 ± 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.
Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist’s assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.
In this work we revisit TV filter and propose an improved version that is tailored to diagnostic CT purposes. We revise TV cost function, which results in symmetric gradient function that leads to more natural noise texture. We apply a multi-scale approach to resolve noise grain issue in CT images. We examine noise texture, granularity, and loss of low contrast in the test images. We also discuss potential acceleration by Nesterov and Conjugate Gradient methods.
Motion estimation is a very important method for improving image quality by compensating the cardiac motion at the best phase reconstructed. We tackle the cardiac motion estimation problem using an image registration approach. We compare the performance of three gradient-based registration methods on clinical data. In addition to simple gradient descent, we test the Nesterov accelerated descent and conjugate gradient algorithms. The results show that accelerated gradient methods provide significant speedup over conventional gradient descent with no loss of image quality.
A new motion estimation and compensation method for cardiac computed tomography (CT) was developed. By
combining two motion estimation (ME) approaches the proposed method estimates the local and global cardiac motion
and then preforms motion compensated reconstruction. The combined motion estimation method has two parts: one is
the local motion estimation, which estimates the coronary artery motion by using coronary artery tree tracking and
registration; the other one is the global motion estimation, which estimates the entire cardiac motion estimation by image
registration. The final cardiac motion is the linear combination of the coronary artery motion and entire cardiac motion
the. We use the backproject-then-warp method proposed by Pack et al. to perform motion compensation reconstruction
(MCR). The proposed method was evaluated with 5 patient data and improvements in sharpness of both coronary
arteries and heart chamber boundaries were obtained.
In addition to seeking geometric correspondence between the inputs, a legitimate image registration algorithm should also
keep the estimated transformation meaningful or regular. In this paper, we present a mathematically sound formulation that
explicitly controls the deformation to keep each grid in a meaningful shape over the entire geometric matching procedure.
The deformation regularity conditions are enforced by maintaining all the moving neighbors as non-twist grids. In contrast
to similar works, our model differentiates and formulates the convex and concave update cases under an efficient and
straightforward point-line/surface orientation framework, and uses equality constraints to guarantee grid regularity and
prevent folding. Experiments on MR images are presented to show the improvements made by our model over the popular
Demon's and DCT-based registration algorithms.