Images Of Breast Tissue Research Articles

A mass classification and image retrieval model for mammograms

In this paper, a breast tissue density classification and image retrieval model is studied and a model for the data reduction is presented. This model is based on two-directional two-dimensional principal component analysis ((2D)2PCA) technique, and a support vector machine (SVM) with the radial basis function (RBF) for mammographic images classification and retrieval. The model is formed based on breast density, according to the categories defined by the breast imaging-reporting and data system (BIRADS) which is a standard on the assessment of mammographic images and is tested on the Mammographic Image Analysis Society (MIAS) database. The five-fold cross-validation has been used for the parameters selection in SVM to avoid the over-fitting error in the data classification. The average precision rates of the model are in the range from 87·34% to 99·12%.

A new automatic image analysis method for assessing estrogen receptors’ status in breast tissue specimens

Manual assessment of estrogen receptors′ (ER) status from breast tissue microscopy images is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to obtain consistent, objective and rapid diagnoses of histopathology specimens. In breast cancer biopsies immunohistochemically (IHC) stained for ER, cancer cell nuclei present a large variety in their characteristics that bring various difficulties for traditional image analysis methods. In this paper, we propose a new automatic method to perform both segmentation and classification of breast cell nuclei in order to give quantitative assessment and uniform indicators of IHC staining that will help pathologists in their diagnostic. Firstly, a color geometric active contour model incorporating a spatial fuzzy clustering algorithm is proposed to detect the contours of all cell nuclei in the image. Secondly, overlapping and touching nuclei are separated using an improved watershed algorithm based on a concave vertex graph. Finally, to identify positive and negative stained nuclei, all the segmented nuclei are classified into five categories according to their staining intensity and morphological features using a trained multilayer neural network combined with Fisher's linear discriminant preprocessing. The proposed method is tested on a large dataset containing several breast tissue images with different levels of malignancy. The experimental results show high agreement between the results of the method and ground-truth from the pathologist panel. Furthermore, a comparative study versus existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method.

Broadband wavelength swept source combining a quantum dot and a quantum well SOA in the wavelength range of 1153–1366 nm

A wide tuning range wavelength swept laser for high axial resolution optical coherence tomography reported. Here, a parallel configuration of a quantum dot semiconductor optical amplifier (SOA) and a quantum well SOA is utilised for broadband optical source. The total scanning range of over 210 nm (192 nm at 10 dB) from 1153 to 1366 nm is reported. To investigate the feasiblity of the swept light source, demonstrated is an optical frequency domain imaging (OFDI) system. The system sensitivity of 101 dB is reported and axial resolution of 6.0 µm in air is achieved. Finally, ex vivo OFDI imaging of human breast tissue is performed.

MO‐D‐141‐08: Multi‐Scale Analysis of Collagen Architecture for Classifying Tumor and Healthy Breast Tissue Images

Purpose: To quantify differences in collagen microarchitecture between healthy human breast tissue and breast cancer images using extended field of view second harmonic generation (EFOV SHG) microscopy coupled with analysis by the curvelet transform. Methods: Two frozen samples of normal associated human breast tissue (NAT) and invasive ductal carcinoma (IDC) were obtained through the University of Wisconsin BioBank. Samples were thawed, fixed, embedded in agar, cut into 300 micron thick sections using a vibratome and imaged using a custom inverted SHG microscope designed to allow for EFOV imaging. Individual SHG Images were captured at 0.79 micron resolution and a FOV of approximately 200 microns. The FOV was extended by stage scanning and image stitching to produce submicron resolution fields of view up to 2 cm2. NAT and IDC tissues were imaged, stitched using the Grid/Collection Stitching ImageJ plugin, and analyzed using features derived from the curvelet transform including variance in orientation and prevalence at various scales. These novel features were tested using support vector machine (SVM) cross validation. Results: IDC tissue contained linearly aligned, crisscrossing collagen while NAT tissue contained wavy, dense collagen. The mean curvelet alignment value for the normal images was approximately 2X higher than that for IDC. In addition, normal images had approximately 20% fewer closely correlated curvelets compared to IDC. Three‐fold cross validation of a linear SVM classifier revealed average sensitivity and specificity of 0.994 and 0.969 respectively. Conclusion: These results demonstrate the excellent potential for collagen architecture quantification based on features derived from the curvelet transform. We have also shown the value of EFOV SHG imaging to capture both large and small scale features necessary for quantifying collagen morphology. Future work will improve EFOV SHG imaging throughput and test classification capabilities of the curvelet based feature set on a large sample population. NIH R01 grants CA114462 and CA136590 to P.J.K. and K.W.E., T32 CA009206 to J.S.B and the Morgridge Institute for Research

Determining breast pathology in surgical margins with high-frequency ultrasound: phantom and numerical simulations

Two parameters in high-frequency ultrasound (20-80 MHz) have been found to be sensitive to a range of pathologies in resected margins from breast conservation surgery: The number of peaks (the peak density) in the waveform spectrum and the slope of the Fourier transform of the waveform spectrum. Previous studies have indicated that peak density and slope may correlate to microscopic heterogeneity in tissue structure, which is modified by atypical and malignant processes. To test this hypothesis, through-transmission and pulse-echo measurements were acquired from gelatin-based phantoms containing polyethylene microspheres and nylon fibers (2.5-10% volume concentration). Multipole methods were also used to model through-transmission measurements of tumor progression in lobular carcinoma in situ. The simulated breast tissue contained 1000-2000 nucleated cells with random lobular cavities. The peak densities of the heterogeneous phantoms were significantly greater than those of the homogeneous control samples, whereas the slopes were less. Similarly, the models produced spectra with peak densities that increased with malignant cell proliferation. The results are consistent with breast tissue data, and provide a physical mechanism for the use of peak density and slope in the imaging of breast tissues with atypical and malignant pathologies. This work was supported by Utah Valley University.

Human hand radiography using X-ray differential phase contrast combined with dark-field imaging

Established X-ray-based imaging procedures such as conventional radiography and computed tomography (CT) rely on the interaction of photons when passing through tissue, including the Compton scattering and the photoelectric effect, which is influenced by the X-ray energy and the type of matter. The resulting mean attenuation of X-rays can be measured and depicted on images with different gray levels. X-ray phase contrast imaging (PCI) represents a relatively new imaging technique relying upon the refraction of Xrays. As such, PCI relies on a fundamentally different physical contrast mechanism compared with conventional, absorption-based X-ray imaging. In the energy range of diagnostic imaging (10–120 keV), refraction is the dominant effect over absorption, but more difficult to acquire. Previous studies have demonstrated that PCI can provide considerably higher contrast in soft tissue, giving rise to its application in fields where conventional radiography and CT are usually limited. Among a variety of techniques used to acquire phase contrast images, grating interferometry [1] has recently attracted great attention because of its compatibility with conventional X-ray tubes [2, 3], which is the key prerequisite for the clinical applicability. In addition, this technique provides a third contrast mode along with absorption and phase contrast, which is the dark-field contrast [4]. Similarly, dark-field imaging again exploits a physically different interaction mechanism and represents the intensity of the scattered X-rays within the area of a single detector pixel. Image pixels with high gray values indicate strong scattering. Recent studies have investigated the performance of phase contrast (PC) and dark-field contrast (DC) in the imaging of female breast tissue, indicating promising results for distinguishing microcalcifications and the malignant conversion or extension of the carcinoma into normal breast tissue [5, 6]. Yet, joint pathologies such as rheumatoid arthritis, crystal arthropathies, and connective tissue diseases (e.g., scleroderma), are also associated with soft tissue affection and occasional calcifications. Conventional radiography of the hand is a cornerstone imaging study for the detection and monitoring of joint diseases as subtle changes of joint space and bones (narrowing and erosions or osteophytes) and—if perceivable—of soft tissue (including calcifications and fibrosis) [7, 8], indicating disease activity and/or progress. While tissue evaluation with conventional radiography is based on morphological criteria Electronic supplementary material The online version of this article (doi:10.1007/s00256-013-1606-7) contains supplementary material, which is available to authorized users T. Thuring (*) : Z. Wang :C. David :M. Stampanoni Paul Scherrer Institut, WBBA/213, 5232, Villigen, Switzerland e-mail: thomas.thuering@psi.ch

SEGMENTATION AND CORRELATION OF OPTICAL COHERENCE TOMOGRAPHY AND X-RAY IMAGES FOR BREAST CANCER DIAGNOSTICS

Pre-operative X-ray mammography and intraoperative X-ray specimen radiography are routinely used to identify breast cancer pathology. Recent advances in optical coherence tomography (OCT) have enabled its use for the intraoperative assessment of surgical margins during breast cancer surgery. While each modality offers distinct contrast of normal and pathological features, there is an essential need to correlate image-based features between the two modalities to take advantage of the diagnostic capabilities of each technique. We compare OCT to X-ray images of resected human breast tissue and correlate different tissue features between modalities for future use in real-time intraoperative OCT imaging. X-ray imaging (specimen radiography) is currently used during surgical breast cancer procedures to verify tumor margins, but cannot image tissue in situ. OCT has the potential to solve this problem by providing intraoperative imaging of the resected specimen as well as the in situ tumor cavity. OCT and micro-CT (X-ray) images are automatically segmented using different computational approaches, and quantitatively compared to determine the ability of these algorithms to automatically differentiate regions of adipose tissue from tumor. Furthermore, two-dimensional (2D) and three-dimensional (3D) results are compared. These correlations, combined with real-time intraoperative OCT, have the potential to identify possible regions of tumor within breast tissue which correlate to tumor regions identified previously on X-ray imaging (mammography or specimen radiography).

Automated classification of immunostaining patterns in breast tissue from the human protein atlas

Background: The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

Three-dimensional fluorescence tomography of human breast tissues in vivo using a hand-held optical imager

Diffuse optical imaging using non-ionizing radiation is a non-invasive method that shows promise towards breast cancer diagnosis. Hand-held optical imagers show potential for clinical translation of the technology, yet they have not been used towards 3D tomography. Herein, 3D tomography of human breast tissue in vivo is demonstrated for the first time using a hand-held optical imager with automated coregistration facilities. Simulation studies are performed on breast geometries to demonstrate the feasibility of 3D tomographic imaging using a hand-held imager under perfect (1:0) and imperfect (100:1, 50:1) fluorescence absorption contrast ratios. Experimental studies are performed in vivo using a 1 µM ICG filled phantom target placed non-invasively underneath the flap of the breast tissue. Results show the ability to perform automated tracking and coregistered imaging of human breast tissue (with tracking accuracy on the order of ∼1 cm). Three-dimensional tomography results demonstrated the ability to recover a single target placed at a depth of 2.5 cm, from both the simulated (at 1:0, 100:1 and 50:1 contrasts) and experimental cases on actual breast tissues. Ongoing efforts to improve target depth recovery are carried out via implementation of transmittance imaging in the hand-held imager.

Grating-based darkfield imaging of human breast tissue

Mastectomy specimens were investigated using a Talbot-Lau X-ray imaging set-up. Significant structures in the darkfield were observed, which revealed considerably higher contrast than those observed in digital mammography. Comparison with the histomorphometric image proofs that the darkfield signal correlates with a tumor region containing small calcification grains of 3 to 30μm size.

CT Scan Technique Boosts 3D Mammography

Abstract Using phase contrast tomography, researchers at the University of California, Los Angeles, and their colleagues have produced high-resolution 3D images of breast tissue with radiation exposure that is less than that from today's 2-dimensional screening mammograms.

Non‐Gaussian statistical properties of breast images

Several studies have shown that the power spectrum of x-ray breast images is well described by a power-law at lower frequencies where anatomical variability dominates. However, an image generated from a Gaussian process with this spectrum is easily distinguished from an image of actual breast tissue by eye. This demonstrates that higher order non-Gaussian statistical properties of mammograms are readily accessible to the visual system. The authors' purpose is to quantify and characterize non-Gaussian statistical properties of breast images as influenced by processing of a digital mammogram, different imaging modalities, and breast density. To quantify non-Gaussian statistical properties, the authors consider histograms of filter responses from the interior of a breast image that have similar properties to receptive fields in the early visual system. They quantify departure from a Gaussian distribution by the relative entropy of the histogram compared to a best-fit Gaussian distribution. This entropy is normalized by the relative entropy of a best-fit Laplacian distribution into a measure they refer to as Laplacian fractional entropy (LFE). They test the LFE on a set of 26 patients recalled at screening for which they have available full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT (bCT) images as well as breast density scores and biopsy results. A study of LFE in FFDM comparing the raw "for-processing" transmission data from the device to log-converted density estimates and the processed "for-display" data shows that processing mammographic image data enhances the non-Gaussian content of the image. A check of the methodology using a Gaussian process with a power-law power spectrum shows relatively little bias from the finite extent of the region of interests used. A second study comparing LFE across FFDM, DBT, and bCT modalities shows that each maximized the non-Gaussian content of the image for different ranges of spatial frequency. FFDM is optimal at high spatial frequencies (>0.7 mm(-1)), DBT is optimal at mid-range frequencies (0.3-0.7 mm(-1)), and bCT is optimal at low spatial frequency (<0.3 mm(-1)). A third study of breast density in FFDM and bCT shows that LFE generally rises slightly going from the low-to moderate density, and then falls considerably at higher densities. Non-Gaussian statistical structure in breast images that is manifest in the responses of Gabor filters similar to receptive fields of the early visual system is dependent on how the image data are processed, the modality used to acquire the image, and the density of the breast tissue being imaged. Higher LFE corresponds with expected improvements from image processing and 3D imaging.

Applications of X-Ray Diffraction Imaging to Mammography

X-ray diffraction (XRD) imaging appears as an alternative to conventional (transmission) imaging for characterization of breast tissue. The diffraction pattern of a material depends upon its arrangement at the sub-molecular level, which is altered as cancer progresses. For this reason the diffraction patterns of normal and neoplastic tissue have a detectable difference, unlike their X-ray attenuation coefficients, which underpin conventional imaging. This paper presents key studies in the field of single-point characterization of breast tissue using XRD. It then describes the development of XRD imaging and the main approaches used. Existing studies in the field of XRD imaging of breast tissue, both in the Computed Tomography modality, allowing cross-sectional images of an object, and in the planar geometry, are compared. Finally, future developments, including the adaptation to breast tissue analysis of patented diffraction imaging techniques and technologies, are discussed.

Wavelet-Based Adaptive Multiresolution Inversion for Quantitative Microwave Imaging of Breast Tissues

Microwave imaging is a feasible tool for medical imaging owing to the different electric properties of tissues. To this end, it is important to devise inversion procedures capable of achieving a reliable quantitative estimate of the tissues under test. With respect to breast screening, we propose a full-wave inversion method, which takes advantage of the adaptive multiresolution features of the wavelet basis to accommodate the trade off between spatial resolution and inversion stability. As shown through a preliminary numerical assessment on 2D anthropomorphic breast phantoms, the proposed approach is capable of achieving satisfying results without any a priori knowledge of breast shape, as well as of thickness and electric properties of the skin layer.

Hand-held optical imager (Gen-2): improved instrumentation and target detectability

Hand-held optical imagers are developed by various researchers towards reflectance-based spectroscopic imaging of breast cancer. Recently, a Gen-1 handheld optical imager was developed with capabilities to perform two-dimensional (2-D) spectroscopic as well as three-dimensional (3-D) tomographic imaging studies. However, the imager was bulky with poor surface contact (~30%) along curved tissues, and limited sensitivity to detect targets consistently. Herein, a Gen-2 hand-held optical imager that overcame the above limitations of the Gen-1 imager has been developed and the instrumentation described. The Gen-2 hand-held imager is less bulky, portable, and has improved surface contact (~86%) on curved tissues. Additionally, the forked probe head design is capable of simultaneous bilateral reflectance imaging of both breast tissues, and also transillumination imaging of a single breast tissue. Experimental studies were performed on tissue phantoms to demonstrate the improved sensitivity in detecting targets using the Gen-2 imager. The improved instrumentation of the Gen-2 imager allowed detection of targets independent of their location with respect to the illumination points, unlike in Gen-1 imager. The developed imager has potential for future clinical breast imaging with enhanced sensitivity, via both reflectance and transillumination imaging.

Design and evaluation of a hybrid photoacoustic tomography and diffuse optical tomography system for breast cancer detection

The authors present the design and evaluation of a second-generation (G2) hybrid photoacoustic tomography (PAT) and diffuse optical tomography (DOT) imaging system for detection of breast cancer. The combined PAT/DOT system takes full advantages of two different modalities to provide complementary information and most accurate recovery of tissue optical properties. This system was designed to produce cross-section images of breast tissue with a ring-shaped home-made array of 64 wideband acoustic transducers combined with an array of 16/16 source/detector optical fiber bundles. A scanning light delivery system was built to illuminate a large area of breast tissue for optimized tissue penetration. Finite element reconstruction methods for quantitative PAT and DOT were utilized to recover absorption and scattering coefficients of tissue-mimicking phantoms and ex vivo tumor tissue. Performances of the acoustic transducer array and the PAT/DOT system were investigated in detail. Frequency response of the transducers is from 380 kHz to 1.48 MHz and maximum frequency response is up to 2 MHz. Directivity of a single element in the array is ±30°. The PAT/DOT system offers a spatial resolution of ∼0.5 mm for PAT and of ∼4.0 mm for DOT. Quantitatively accurate absorption and scattering coefficients were obtained from both the phantom and ex vivo experiments. The PAT/DOT system provided better PAT images when the targets were smaller in size and located near the center of the background phantom, while better DOT images were obtained when the targets were larger in size and located away from the center of the background. The successful results obtained from both the phantom and ex vivo experiments will allow us to test the hybrid system in humans with breast cancer in the near future.

Cancer Cells Detection and Classification in Biopsy Image

this research work, to understand the types of cancer cell and attempt to analyses the biopsy slides. In this method to identify cancer parts just using simple technique of isolation of insignificant portion of biopsy slide by cancer cell level and object level segmentation and classification. Many features used in the cancer cell detection and classification of biopsy image are inspired by clinical pathologists as important for diagnosis, prognosis and characterization. A large majority of these features are features of cell nuclei in biopsy image; as such, there is often the desire to segment the image into individual cell nuclei and cancer object. In this paper, present an analysis of the utility of color Thresholding, adaptive Thresholding and watershed method for segmentation of cancer cell nuclei for classification of H&E stained histopathology image of breast tissue using neural network. This paper showing the cell level and object level classification performance using these segmented nuclei in a benign versus malignant. Results indicate that very good segmentation and classification accuracies can be achieved with color Thresholding, adaptive Thresholding, watershed based segmentation of cancer cell nuclei and cancer objects and classification of biopsy image.

Multiple Irradiations by Hybrid Source for Early Breast Carcinoma Detection

Early detection of breast carcinoma increases the survival rate; physical and psychological trauma could also be avoided to a greater extent by early detection and treatment. Though there are several diagnostic methods, each method is having its own merits and demerits. In this paper,a novel method is proposed to produce an active thermogram using external hybrid irradiations to increase the contrast between the thermal images of normal breast tissues and the cancer tissues. In hybrid irradiation technique the phantom models were irradiated using FIR and microwave sources. Results of the experiments conducted using phantom models showed the temperature difference between the cancerous and normal breast tissues was increased by FIR irradiation (first irradiation) nominally; the second irradiation (Microwave irradiation) considerably elevated the temperature difference between the normal and breast tissues. The third irradiation (subsequent Microwave irradiation) did not have any impact. The hybrid irradiation technique produces a temperature difference of about 5 .75°c between the normal and cancerous breast tissues for a cancer tumor which produces a temperature difference of only 1°c between normal and cancerous tissues by passive radiation. Hence there is a marked intensity variation between the normal and cancerous tissues of the breast in thermogram, which makes the early diagnosis of breast carcinoma possible. Since even the third irradiation did not have any impact on the temperature difference between the normal and cancer breast tissues, hybrid multiple irradiations are not of any use. Based on the experimental results, thermograms were simulated by taking a normal breast thermogam as the base image. MATLAB7.1 is used for simulation. Actual thermograms were produced by a FLIR thermal imager (FLIR system – Model FLIR i5-Sweden).

MICROWAVE CONTRAST IMAGING OF BREAST TISSUE FROM LOCAL VELOCITY ESTIMATION

This paper proposes a new method to display microwave images of breast tissue, based on estimation of local microwave velocity from time of ∞ight measurements. Its computational demands are low compared with tomography. It has two major components: 1) the estimation of the travel time of microwaves across the tissue between a set of antennae using a wavelet decomposition, and 2) the estimation of the microwave velocity fleld from the set of travel times using a low dimensional set of radial basis functions to model local velocity. The technique is evaluated in 2-D on clinical MR-based numerical breast phantoms incorporated in Finite-Difierence Time-Domain simulations. The basis functions, used with a regularisation scheme to improve numerical stability, reduce the dimensionality of the inverse problem for computational e-ciency and also to improve the robustness to error in velocity estimation. The results support previously published flndings that the wavelet transform is suitable for robust measurement of time of ∞ight even in clinically signiflcant simulations, and shows that the velocity contrast images can be constructed so difierent regions of breast tissue type can be distinguished. In particular, the presence of a tumour is clearly detected, demonstrating the potential of this approach for breast screening. Keywords: Biomedical signal processing; Microwave imaging; Image reconstruction.

Mouse cursor movement and eye tracking data as an indicator of pathologists' attention when viewing digital whole slide images

Context: Digital pathology has the potential to dramatically alter the way pathologists work, yet little is known about pathologists' viewing behavior while interpreting digital whole slide images. While tracking pathologist eye movements when viewing digital slides may be the most direct method of capturing pathologists' viewing strategies, this technique is cumbersome and technically challenging to use in remote settings. Tracking pathologist mouse cursor movements may serve as a practical method of studying digital slide interpretation, and mouse cursor data may illuminate pathologists' viewing strategies and time expenditures in their interpretive workflow. Aims: To evaluate the utility of mouse cursor movement data, in addition to eye-tracking data, in studying pathologists' attention and viewing behavior. Settings and Design: Pathologists (N = 7) viewed 10 digital whole slide images of breast tissue that were selected using a random stratified sampling technique to include a range of breast pathology diagnoses (benign/atypia, carcinoma in situ, and invasive breast cancer). A panel of three expert breast pathologists established a consensus diagnosis for each case using a modified Delphi approach. Materials and Methods: Participants' foveal vision was tracked using SensoMotoric Instruments RED 60 Hz eye-tracking system. Mouse cursor movement was tracked using a custom MATLAB script. Statistical Analysis Used: Data on eye-gaze and mouse cursor position were gathered at fixed intervals and analyzed using distance comparisons and regression analyses by slide diagnosis and pathologist expertise. Pathologists' accuracy (defined as percent agreement with the expert consensus diagnoses) and efficiency (accuracy and speed) were also analyzed. Results: Mean viewing time per slide was 75.2 seconds (SD = 38.42). Accuracy (percent agreement with expert consensus) by diagnosis type was: 83% (benign/atypia); 48% (carcinoma in situ); and 93% (invasive). Spatial coupling was close between eye-gaze and mouse cursor positions (highest frequency Δx was 4.00px (SD =16.10), and Δy was 37.50px (SD = 28.08)). Mouse cursor position moderately predicted eye gaze patterns (Rx = 0.33 and Ry = 0.21). Conclusions: Data detailing mouse cursor movements may be a useful addition to future studies of pathologists' accuracy and efficiency when using digital pathology.