Disparity Estimation on Stereo Mammograms.

We consider the problem of depth estimation on digital stereo mammograms. Being able to elucidate 3D information from stereo mammograms is an important precursor to conducting 3D digital analysis of data from this promising new modality. The problem is generally much harder than the classic stereo matching problem on visible light images of the natural world, since nearly all of the 3D structural information of interest exists as complex network of multilayered, heavily occluded curvilinear structures. Toward addressing this difficult problem, we formulate a new stereo model that minimizes a global energy functional to densely estimate disparity on stereo mammogram images, by introducing a new singularity index as a constraint to obtain better estimates of disparity along critical curvilinear structures. Curvilinear structures, such as vasculature and spicules, are particularly salient structures in the breast, and being able to accurately position them in 3D is a valuable goal. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model.

Stereoscopic interpretation of low-dose breast tomosynthesis projection images.

The purpose of this study was to evaluate stereoscopic perception of low-dose breast tomosynthesis projection images. In this Institutional Review Board exempt study, craniocaudal breast tomosynthesis cases (N = 47), consisting of 23 biopsy-proven malignant mass cases and 24 normal cases, were retrospectively reviewed. A stereoscopic pair comprised of two projection images that were ±4° apart from the zero angle projection was displayed on a Planar PL2010M stereoscopic display (Planar Systems, Inc., Beaverton, OR, USA). An experienced breast imager verified the truth for each case stereoscopically. A two-phase blinded observer study was conducted. In the first phase, two experienced breast imagers rated their ability to perceive 3D information using a scale of 1-3 and described the most suspicious lesion using the BI-RADS® descriptors. In the second phase, four experienced breast imagers were asked to make a binary decision on whether they saw a mass for which they would initiate a diagnostic workup or not and also report the location of the mass and provide a confidence score in the range of 0-100. The sensitivity and the specificity of the lesion detection task were evaluated. The results from our study suggest that radiologists who can perceive stereo can reliably interpret breast tomosynthesis projection images using stereoscopic viewing.

Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory.

Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.

Active segmentation of 3D axonal images.

We present an active contour framework for segmenting neuronal axons on 3D confocal microscopy data. Our work is motivated by the need to conduct high throughput experiments involving microfluidic devices and femtosecond lasers to study the genetic mechanisms behind nerve regeneration and repair. While most of the applications for active contours have focused on segmenting closed regions in 2D medical and natural images, there haven't been many applications that have focused on segmenting open-ended curvilinear structures in 2D or higher dimensions. The active contour framework we present here ties together a well known 2D active contour model [5] along with the physics of projection imaging geometry to yield a segmented axon in 3D. Qualitative results illustrate the promise of our approach for segmenting neruonal axons on 3D confocal microscopy data.

A shape constrained parametric active contour model for breast contour detection.

Quantitative measures of breast morphology can help a breast cancer survivor to understand outcomes of reconstructive surgeries. One bottleneck of quantifying breast morphology is that there are only a few reliable automation algorithms for detecting the breast contour. This study proposes a novel approach for detecting the breast contour, which is based on a parametric active contour model. In addition to employing the traditional parametric active contour model, the proposed approach enforces a mathematical shape constraint based on the catenary curve, which has been previously shown to capture the overall shape of the breast contour reliably. The mathematical shape constraint regulates the evolution of the active contour and helps the contour evolve towards the breast, while minimizing the undesired effects of other structures such as, the nipple/areola and scars. The efficacy of the proposed approach was evaluated on anterior posterior photographs of women who underwent or were scheduled for breast reconstruction surgery including autologous tissue reconstruction. The proposed algorithm shows promising results for detecting the breast contour.

Computer-aided diagnosis in breast magnetic resonance imaging.

In this paper, we review the role played by breast magnetic resonance imaging in the detection and diagnosis of breast cancer. This is followed by a discussion of clinical decision support systems in medicine and their contributions in breast magnetic resonance imaging interpretation. We conclude by discussing the future of computer-aided diagnosis in breast magnetic resonance imaging.

Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain.

Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional region-based formulations model local region intensity by assuming a piecewise constant approximation. However, the piecewise constant approximation rarely holds true for medical images such as MR images due to the presence of noise and bias field, which invariably results in a poor segmentation of the image. To overcome this problem, we have developed a probabilistic region-based active contour model for automatic segmentation of MR images of the brain. In our approach, a mixture of Gaussian distributions is used to accurately model the arbitrarily shaped local region intensity distribution. Prior spatial information derived from probabilistic atlases is also integrated into the level set evolution framework for guiding the segmentation process. Our experiments with a series of publicly available brain MR images show that the proposed active contour model gives stable and accurate segmentation results when compared to the traditional region based formulations.

Snakules: a model-based active contour algorithm for the annotation of spicules on mammography.

We have developed a novel, model-based active contour algorithm, termed "snakules", for the annotation of spicules on mammography. At each suspect spiculated mass location that has been identified by either a radiologist or a computer-aided detection (CADe) algorithm, we deploy snakules that are converging open-ended active contours also known as snakes. The set of convergent snakules have the ability to deform, grow and adapt to the true spicules in the image, by an attractive process of curve evolution and motion that optimizes the local matching energy. Starting from a natural set of automatically detected candidate points, snakules are deployed in the region around a suspect spiculated mass location. Statistics of prior physical measurements of spiculated masses on mammography are used in the process of detecting the set of candidate points. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an image analysis technique to improve the specificity of CADe algorithms and as a CADe prompting tool.

Evaluation of stylus for radiographic image annotation.

We evaluated the use of a stylus as a computer interface for radiographic image annotation. Our case study concerned the annotation of spiculated lesions on mammograms. Three experienced radiologists annotated 20 mammograms depicting spiculated lesions. We evaluated the interobserver agreement in annotations marked with a stylus versus those marked with a mouse using the intraclass correlation coefficient. Better agreement in annotating spicule width was observed with the stylus, suggesting that it is easier to accurately annotate subtle regions on an image using a stylus.

Comparison of pre-processing techniques for fluorescence microscopy images of cells labeled for actin.

Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.

Computer-aided detection of breast cancer - have all bases been covered?

The use of computer-aided detection (CAD) systems in mammography has been the subject of intense research for many years. These systems have been developed with the aim of helping radiologists to detect signs of breast cancer. However, the effectiveness of CAD systems in practice has sparked recent debate. In this commentary, we argue that computer-aided detection will become an increasingly important tool for radiologists in the early detection of breast cancer, but there are some important issues that need to be given greater focus in designing CAD systems if they are to reach their full potential.