Laura E. Boucheron

Histopathological Image Analysis: A Review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Propranolol treatment of infantile hemangioma endothelial cells: A molecular analysis

Infantile hemangiomas (IHs) are non-malignant, largely cutaneous vascular tumors affecting approximately 5–10% of children to varying degrees. During the first year of life, these tumors are strongly proliferative, reaching an average size ranging from 2 to 20 cm. These lesions subsequently stabilize, undergo a spontaneous slow involution and are fully regressed by 5 to 10 years of age. Systemic treatment of infants with the non-selective β-adrenergic receptor blocker, propranolol, has demonstrated remarkable efficacy in reducing the size and appearance of IHs. However, the mechanism by which this occurs is largely unknown. In this study, we sought to understand the molecular mechanisms underlying the effectiveness of β blocker treatment in IHs. Our data reveal that propranolol treatment of IH endothelial cells, as well as a panel of normal primary endothelial cells, blocks endothelial cell proliferation, migration, and formation of the actin cytoskeleton coincident with alterations in vascular endothelial growth factor receptor-2 (VEGFR-2), p38 and cofilin signaling. Moreover, propranolol induces major alterations in the protein levels of key cyclins and cyclin-dependent kinase inhibitors, and modulates global gene expression patterns with a particular affect on genes involved in lipid/sterol metabolism, cell cycle regulation, angiogenesis and ubiquitination. Interestingly, the effects of propranolol were endothelial cell-type independent, affecting the properties of IH endothelial cells at similar levels to that observed in neonatal dermal microvascular and coronary artery endothelial cells. This data suggests that while propranolol markedly inhibits hemangioma and normal endothelial cell function, its lack of endothelial cell specificity hints that the efficacy of this drug in the treatment of IHs may be more complex than simply blockage of endothelial function as previously believed.

Targeting of Beta Adrenergic Receptors Results in Therapeutic Efficacy against Models of Hemangioendothelioma and Angiosarcoma

Therapeutic targeting of the beta-adrenergic receptors has recently shown remarkable efficacy in the treatment of benign vascular tumors such as infantile hemangiomas. As infantile hemangiomas are reported to express high levels of beta adrenergic receptors, we examined the expression of these receptors on more aggressive vascular tumors such as hemangioendotheliomas and angiosarcomas, revealing beta 1, 2, and 3 receptors were indeed present and therefore aggressive vascular tumors may similarly show increased susceptibility to the inhibitory effects of beta blockade. Using a panel of hemangioendothelioma and angiosarcoma cell lines, we demonstrate that beta adrenergic inhibition blocks cell proliferation and induces apoptosis in a dose dependent manner. Beta blockade is selective for vascular tumor cells over normal endothelial cells and synergistically effective when combined with standard chemotherapeutic or cytotoxic agents. We demonstrate that inhibition of beta adrenergic signaling induces large scale changes in the global gene expression patterns of vascular tumors, including alterations in the expression of established cell cycle and apoptotic regulators. Using in vivo tumor models we demonstrate that beta blockade shows remarkable efficacy as a single agent in reducing the growth of angiosarcoma tumors. In summary, these experiments demonstrate the selective cytotoxicity and tumor suppressive ability of beta adrenergic inhibition on malignant vascular tumors and have laid the groundwork for a promising treatment of angiosarcomas in humans.

On the inversion of Mel-frequency cepstral coefficients for speech enhancement applications

The use of Mel-frequency cepstral coefficients (MFCCs) is well established in the fields of speech processing, particularly for speaker modeling within a Gaussian mixture model (GMM) speaker recognition system. The use of GMMs for speech enhancement applications has only recently been proposed in the literature; the concept of direct inversion of the MFCCs, however, has not been studied. In this paper we present a means to invert MFCCs for use in speech enhancement applications. Results for cepstral inversion is evaluated on the TIMIT speech corpus using perceptual evaluation of speech quality (PESQ).

Low Bit-Rate Speech Coding Through Quantization of Mel-Frequency Cepstral Coefficients

In this paper, we propose a low bit-rate speech codec based on vector quantization (VQ) of the mel-frequency cepstral coefficients (MFCCs). We begin by showing that if a high-resolution mel-frequency cepstrum (MFC) is computed, good-quality speech reconstruction is possible from the MFCCs despite the lack of phase information. By evaluating the contribution toward speech quality that individual MFCCs make and applying appropriate quantization, our results show that the MFCC-based codec exceeds the state-of-the-art MELPe codec across the entire range of 600-2400 bps, when evaluated with the perceptual evaluation of speech quality (PESQ) (ITU-T recommendation P.862). The main advantage of the proposed codec is in distributed speech recognition (DSR) since the MFCCs can be directly applied thus eliminating additional decode and feature extract stages; furthermore, the proposed codec better preserves the fidelity of MFCCs and better word accuracy rates as compared to CELP and MELPe codecs.

Lossless wavelet-based compression of digital elevation maps for fast and efficient search and retrieval

We consider here the implementation of efficient search and retrieval of digital elevation maps (DEMs), in particular the ability to conduct elevation searches without decompressing the entire map. We utilize set partitioning in hierarchical trees (SPIHT) for the compression, and compare two wavelet-based search and retrieval systems: one utilizing nonlinear max- and min-lifted integer wavelets and the other using the standard 5/3 integer wavelet. While the coarse-scale maxima/minima preservation inherent in the max- and min-lifted wavelets may seem ideal for the implementation of efficient elevation searches, the interscale propagation of maxima/minima and the bit-plane layering of the SPIHT coder adversely affect the efficiency of such searches. The use of the standard 5/3 integer wavelet requires the addition of separate maxima and minima information but does not have the complication of coefficient propagation, providing more satisfactory results. Elevation search bitrates are presented for both systems and compared to results obtained using the Kakadu implementation of the JPEG2000 standard.

Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Many features used in the analysis of pathology imagery are inspired by grading features defined by clinical pathologists as important for diagnosis and characterization. A large majority of these features are features of cell nuclei; as such, there is often the desire to segment the imagery into individual nuclei prior to feature extraction and further analysis. In this paper we present an analysis of the utility of imperfectly segmented cell nuclei for classification of H&E stained histopathology imagery of breast tissue. We show the object- and image-level classification performance using these imperfectly segmented nuclei in a benign versus malignant decision. Results indicate that very good classification accuracies can be achieved with imperfectly segmented nuclei and further that perfect nuclei segmentation does not necessarily guarantee better classification accuracy.

PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a \emph{geostationary operational environmental satellite} (\emph{GOES}) class. When we additionally consider non-flaring regions, we find an increased average error of approximately 3/4 a \emph{GOES} class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.

Hybrid Scalar/Vector Quantization of Mel-Frequency Cepstral Coefficients for Low Bit-Rate Coding of Speech

In this paper, we propose a low bit-rate speech codec based on a hybrid scalar/vector quantization of the mel-frequency cepstral coefficients (MFCCs). We begin by showing that if a high-resolution mel-frequency cepstrum (MFC) is computed, good-quality speech reconstruction is possible from the MFCCs despite the lack of explicit phase information. By evaluating the contribution toward speech quality that individual MFCCs make and applying appropriate quantization, our results show perceptual evaluation of speech quality (PESQ) of the MFCC-based codec matches the state-of-the-art MELPe codec at 600 bps and exceeds the CELP codec at 2000 -- 4000 bps coding rates. The main advantage of the proposed codec is in distributed speech recognition (DSR) since speech features based on MFCCs can be directly obtained from code words thus eliminating additional decode and feature extract stages.

Morphological restriction of human coronary artery endothelial cells substantially impacts global gene expression patterns

Alterations in cell shape have been shown to modulate chromatin condensation and cell lineage specification; however, the mechanisms controlling these processes are largely unknown. Because endothelial cells experience cyclic mechanical changes from blood flow during normal physiological processes and disrupted mechanical changes as a result of abnormal blood flow, cell shape deformation and loss of polarization during coronary artery disease, we aimed to determine how morphological restriction affects global gene expression patterns. Human coronary artery endothelial cells (HCAECs) were cultured on spatially defined adhesive micropatterns, forcing them to conform to unique cellular morphologies differing in cellular polarization and angularity. We utilized pattern recognition algorithms and statistical analysis to validate the cytoskeletal pattern reproducibility and uniqueness of each micropattern, and performed microarray analysis on normal‐shaped and micropatterned HCAECs to determine how constrained cellular morphology affects gene expression patterns. Analysis of the data revealed that forcing HCAECs to conform to geometrically‐defined shapes significantly affects their global transcription patterns compared to nonrestricted shapes. Interestingly, gene expression patterns were altered in response to morphological restriction in general, although they were consistent regardless of the particular shape the cells conformed to. These data suggest that the ability of HCAECs to spread, although not necessarily their particular morphology, dictates their genomics patterns.