Sebastian Berisha, Ph.D.

Postdoctoral Fellowship, University of Houston

2015 - 2018

IEEE Transactions on Geoscience and Remote Sensing: top 15 reviewer of the year

2017

BIM-Sim: Interactive Simulation of Broadband Imaging Using Mie Theory

Berisha, S., van Dijk, T., Bhargava, R., Carney, P.S., Mayerich, D.

2017

Understanding the structure of a scattered electromagnetic (EM) field is critical to improving the imaging process. Mechanisms such as diffraction, scattering, and interference affect an image, limiting the resolution, and potentially introducing artifacts. Simulation and visualization of scattered fields thus plays an important role in imaging science. However, EM fields are high-dimensional, making them time-consuming to simulate, and difficult to visualize. In this paper, we present a framework for interactively computing and visualizing EM fields scattered by micro and nano-particles. Our software uses graphics hardware for evaluating the field both inside and outside of these particles. We then use Monte-Carlo sampling to reconstruct and visualize the three-dimensional structure of the field, spectral profiles at individual points, the structure of the field at the surface of the particle, and the resulting image produced by an optical system.

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Phase retrieval based deconvolution algorithm in optical systems

Qin, S., Berisha, S., Mayerich, D., Han, Z.

2017

In an optical imaging system, the retrieved image of an object is blurred by the point spread function (PSF) of the system, and cannot exactly represent the object. Deconvolution is an effective method to recover the object from the blurred image and improve the resolution of the optical system. But in real optical system, the detector only measures the intensity of the light, not the phase. So, the information used in image deconvolution is incomplete and reduces the quality of the final result. Fortunately, phase retrieval theory provides an effective method to recover the complex field from an intensity image. With the phase information, we can get use the complete field information to reconstruct the object more accurately. In this paper, we propose a method based on the phase retrieval to recover the object from the image. Simulation results indicate that our proposed method has improved performance.

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Mitigating fringing in discrete frequency infrared imaging using time-delayed integration

Ran, S., Berisha, S., Mankar, R., Shih, W.C., Mayerich, D.

2018

Infrared (IR) spectroscopic microscopes provide the potential for label-free quantitative molecular imaging of biological samples, which can be used to aid in histology, forensics, and pharmaceutical analysis. Most IR imaging systems use broadband illumination combined with a spectrometer to separate the signal into spectral components. This technique is currently too slow for many biomedical applications such as clinical diagnosis, primarily due to the availability of bright mid-infrared sources and sensitive MCT detectors. There has been a recent push to increase throughput using coherent light sources, such as synchrotron radiation and quantum cascade lasers. While these sources provide a significant increase in intensity, the coherence introduces fringing artifacts in the final image. We demonstrate that applying time-delayed integration in one dimension can dramatically reduce fringing artifacts with minimal alterations to the standard infrared imaging pipeline. The proposed technique also offers the potential for less expensive focal plane array detectors, since linear arrays can be more readily incorporated into the proposed framework.

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Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks

Berisha, S., Lotfollahi, M., Jahanipour, J., Gurcan, I., Walsh, M., Bhargava, R., Van Nguyen, H., Mayerich, D.

2019

Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral and spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.

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Automated osteosclerosis grading of clinical biopsies using infrared spectroscopic imaging

Mankar, R., Bueso-Ramps, C.E., Yin, C.C., Hidalgo-Lopez, J.E., Berisha, S., Kansiz, M., Mayerich, D.

2019

Osteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis, therefore accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson’s trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this paper, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.

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Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning

Lotfollahi, M., Berisha, S., Daeinejad, D., Mayerich, D

2019

Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.

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Three-dimensional GPU-accelerated active contours for automated localization of cells in large images

Lotfollahi, M., Berisha, S., Saadatifard, L., Montier, L., Žiburkus, J., Mayerich, D.

2019

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell localization tasks when compared to existing methods on large 3D brain images.

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