In digital pathology, deep learning approaches have been increasingly applied and shown to be effective in analyzing digitized tissue specimen images. Such approaches have, in general, chosen an arbitrary scale or resolution at which the images are analyzed for several reasons, including computational cost and complexity. However, the tissue characteristics, indicative of cancer, tend to present at differing scales. Herein, we propose a framework that enables deep convolutional neural networks to perform multiscale histological analysis of tissue specimen images in an efficient and effective manner. A deep residual neural network is shared across multiple scales, extracting high-level features. The high-level features from multiple scales are aggregated and transformed in a way that the scale information is embedded in the network. The transformed features are utilized to classify tissue images into cancer and benign. The proposed method is compared to other methodologies to combine the feature from different scales. These competing methods combine the multi-scale features via 1) concatenation 2) addition and 3) convolution. Tissue microarrays (TMAs) were employed to evaluate the proposed method and the other competing methods. Three TMAs, including 225 benign and 377 cancer tissue samples, were used as training dataset. Two TMAs with 151 benign and 252 cancer tissue samples was utilized as testing dataset. The proposed method obtained an accuracy of 0.953 and the area under the receiver operating characteristics curve (AUC) of 0.971 (95% CI: 0.955-0.987), outperforming other competing methods. This suggests that the proposed multiscale approaches via a shared neural network and scale embedding scheme, could aid in improving digital pathology analysis and cancer pathology.
We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step,
we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and
texture-based image features are computed at five different scales, and a multiview boosting method is adopted to
cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize
convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict
cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling,
rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two
tissue microarrays (TMA) – TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185
tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of
0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This
demonstrates that the proposed method can potentially improve prostate cancer pathology.
We recently completed a reader study to compare optical and digital pathology (DP) for the assessment of two tissue-based biomarkers with immunohistochemistry. Eight pathologists reviewed 50 breast cancer whole slides (25 stained with HER2 and 25 with Ki-67) and 2 TMAs (1 stained with HER2, 1 with Ki-67, 97 cores each), using digital and optical microscopy. All reviews took place in a single office, using the same microscope, same computer/color calibrated monitor combination, and the same ambient light, in order to eliminate sources of variability due to these parameters. Agreement analysis was performed using the Kendall’s tau-b metric and percent correct agreement. Results showed relatively high overall inter-observer and inter-modality agreement. However, significant uncertainty was observed for the whole slide evaluation with 95% confidence intervals (CI) in the order of 0.30 for the Kendall’s tau-b metric, despite taking care to reduce sources of uncertainty. For the better-sampled TMAs, CIs were in the order of 0.15. It can be deduced that the sample size of 25 slides for each biomarker was not adequate even though it is in line with recent guidelines for the validation of DP from the College of American Pathologists (20 slides for immunohistochemistry without specifying task). Significant uncertainty was observed in our study, despite controlling for several variables. Further work is needed to identify sources of uncertainty for observer tasks in DP, and to account for it in study designs to assess DP.
Computerized histopathology image analysis enables an objective, efficient, and quantitative assessment of digitized histopathology images. Such analysis often requires an accurate and efficient detection and segmentation of histological structures such as glands, cells and nuclei. The segmentation is used to characterize tissue specimens and to determine the disease status or outcomes. The segmentation of nuclei, in particular, is challenging due to the overlapping or clumped nuclei. Here, we propose a nuclei seed detection method for the individual and overlapping nuclei that utilizes the gradient orientation or direction information. The initial nuclei segmentation is provided by a multiview boosting approach. The angle of the gradient orientation is computed and traced for the nuclear boundaries. Taking the first derivative of the angle of the gradient orientation, high concavity points (junctions) are discovered. False junctions are found and removed by adopting a greedy search scheme with the goodness-of-fit statistic in a linear least squares sense. Then, the junctions determine boundary segments. Partial boundary segments belonging to the same nucleus are identified and combined by examining the overlapping area between them. Using the final set of the boundary segments, we generate the list of seeds in tissue images. The method achieved an overall precision of 0.89 and a recall of 0.88 in comparison to the manual segmentation.
We present a platform for designing and executing studies that compare pathologists interpreting histopathology of whole slide images (WSIs) on a computer display to pathologists interpreting glass slides on an optical microscope. eeDAP is an evaluation environment for digital and analog pathology. The key element in eeDAP is the registration of the WSI to the glass slide. Registration is accomplished through computer control of the microscope stage and a camera mounted on the microscope that acquires real-time images of the microscope field of view (FOV). Registration allows for the evaluation of the same regions of interest (ROIs) in both domains. This can reduce or eliminate disagreements that arise from pathologists interpreting different areas and focuses on the comparison of image quality. We reduced the pathologist interpretation area from an entire glass slide (10 to 30 mm2) to small ROIs (<50 μm2). We also made possible the evaluation of individual cells. We summarize eeDAP’s software and hardware and provide calculations and corresponding images of the microscope FOV and the ROIs extracted from the WSIs. The eeDAP software can be downloaded from the Google code website (project: eeDAP) as a MATLAB source or as a precompiled stand-alone license-free application.
Purpose: The purpose of this work is to present a platform for designing and executing studies that compare pathologists interpreting histopathology of whole slide images (WSI) on a computer display to pathologists interpreting glass slides on an optical microscope. Methods: Here we present eeDAP, an evaluation environment for digital and analog pathology. The key element in eeDAP is the registration of theWSI to the glass slide. Registration is accomplished through computer control of the microscope stage and a camera mounted on the microscope that acquires images of the real time microscope view. Registration allows for the evaluation of the same regions of interest (ROIs) in both domains. This can reduce or eliminate disagreements that arise from pathologists interpreting different areas and focuses the comparison on image quality. Results: We reduced the pathologist interpretation area from an entire glass slide (≈10-30 mm)2 to small ROIs <(50 um)2. We also made possible the evaluation of individual cells. Conclusions: We summarize eeDAP’s software and hardware and provide calculations and corresponding images of the microscope field of view and the ROIs extracted from the WSIs. These calculations help provide a sense of eeDAP’s functionality and operating principles, while the images provide a sense of the look and feel of studies that can be conducted in the digital and analog domains. The eeDAP software can be downloaded from code.google.com (project: eeDAP) as Matlab source or as a precompiled stand-alone license-free application.
Within the complex branching system of the breast, terminal duct lobular units (TDLUs) are the anatomical
location where most cancer originates. With aging, TDLUs undergo physiological involution, re
ected in a
loss of structural components (acini) and a reduction in total number. Data suggest that women undergoing
benign breast biopsies that do not show age appropriate involution are at increased risk of developing breast
cancer. To date, TDLU assessments have generally been made by qualitative visual assessment, rather by
objective quantitative analysis. This paper introduces a technique to automatically estimate a set of quantitative
measurements and use those variables to more objectively describe and classify TDLUs. To validate the accuracy
of our system, we compared the computer-based morphological properties of 51 TDLUs in breast tissues donated
for research by volunteers in the Susan G. Komen Tissue Bank and compared results to those of a pathologist,
demonstrating 70% agreement. Secondly, in order to show that our method is applicable to a wider range
of datasets, we analyzed 52 TDLUs from biopsies performed for clinical indications in the National Cancer
Institute Breast Radiology and Study of Tissues (BREAST) STAMP project and obtained 82% correlation with
visual assessment. Lastly, we demonstrate the ability to uncover novel measures when researching the structural
properties of the acini by applying machine learning and clustering techniques. Through our study we found that
while the number of acini per TDLU increase exponentially with the TDLU diameter, the average elongation
and roundness remain constant.
Histopathologic correlation is an essential component for validation of the radiological findings. There has been
significant advancement in medical imaging technologies, including molecular imaging, such that, it is essential to
establish the system beyond histopathologic correlation, to protein profiling that can be correlated with imaging at
anatomically identical manner for accurate examination. Recently, a novel technology for proteomic profiling has been
established, called "multiplex tissue immunoblotting (MTIB)" which can offer studying multiple protein expression from
a single histology slide. Therefore, we attempted to establish the system to obtain an identical plane between high
resolution imaging and histopathology at microscopic level so that proteomic profiling can be readily performed using
MTIB. A variety of tissues were obtained from autopsy materials and initially scanned with high field MRI (14T) ex vivo
along with the marker for tissue orientation. The histology slides were prepared from post-scanned tissue under the
marker-guidance in order to obtain an identical plane with high resolution imaging. Subsequently, MTIB was carried out
to study expression of proteins of interest and point by point correlation with high resolution imaging was performed at
histogeographically identical manner.
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