The role of Micro-CT in imaging breast cancer specimens.

PURPOSE: The goal of breast cancer surgery is to remove all of the cancer with a minimum of normal tissue, but absence of full 3-dimensional information on the specimen makes this difficult to achieve. METHOD: Micro-CT is a high resolution, X-ray, 3D imaging method, widely used in industry but rarely in medicine. RESULTS: We imaged and analyzed 173 partial mastectomies (129 ductal carcinomas, 14 lobular carcinomas, 28 DCIS). Imaging was simple and rapid. The size and shape of the cancers seen on Micro-CT closely matched the size and shape of the cancers seen at specimen dissection. Micro-CT images of multicentric/multifocal cancers revealed multiple non-contiguous masses. Micro-CT revealed cancer touching the specimen edge for 93% of the 114 cases judged margin positive by the pathologist, and 28 of the cases not seen as margin positive on pathological analysis; cancer occupied 1.55% of surface area when both the pathologist and Micro-CT suggested cancer at the edge, but only 0.45% of surface area for the "Micro-CT-Only-Positive Cases". Thus, Micro-CT detects cancers that touch a very small region of the specimen surface, which is likely to be missed on sectioning. CONCLUSIONS: Micro-CT provides full 3D images of breast cancer specimens, allowing one to identify, in minutes rather than hours, while the patient is in OR, margin-positive cancers together with information on where the cancer touches the edge, in a fashion more accurate than possible from the histology slides alone.

Development of a 2D Image Reconstruction and Viewing System for Histological Images from Multiple Tissue Blocks: Towards High-Resolution Whole-Organ 3D Histological Images.

High-resolution 3D histology image reconstruction of the whole brain organ starts from reconstructing the high-resolution 2D histology images of a brain slice. In this paper, we introduced a method to automatically align the histology images of thin tissue sections cut from the multiple paraffin-embedded tissue blocks of a brain slice. For this method, we employed template matching and incorporated an optimization technique to further improve the accuracy of the 2D reconstructed image. In the template matching, we used the gross image of the brain slice as a reference to the reconstructed 2D histology image of the slice, while in the optimization procedure, we utilized the Jaccard index as the metric of the reconstruction accuracy. The results of our experiment on the initial 3 different whole-brain tissue slices showed that while the method works, it is also constrained by tissue deformations introduced during the tissue processing and slicing. The size of the reconstructed high-resolution 2D histology image of a brain slice is huge, and designing an image viewer that makes particularly efficient use of the computing power of a standard computer used in our laboratories is of interest. We also present the initial implementation of our 2D image viewer system in this paper.

The Role of Micro-CT in 3D Histology Imaging.

OBJECTIVES: 3D histology tissue modeling is a useful analytical technique for understanding anatomy and disease at the cellular level. However, the current accuracy of 3D histology technology is largely unknown, and errors, misalignment and missing information are common in 3D tissue reconstruction. We used micro-CT imaging technology to better understand these issues and the relationship between fresh tissue and its 3D histology counterpart. METHODS: We imaged formalin-fixed and 2% Lugol-stained mouse brain, human uterus and human lung tissue with micro-CT. We then conducted image analyses on the tissues before and after paraffin embedding using 3D Slicer and ImageJ software to understand how tissue changes between the fixation and embedding steps. RESULTS: We found that all tissue samples decreased in volume by 19.2-61.5% after embedding, that micro-CT imaging can be used to assess the integrity of tissue blocks, and that micro-CT analysis can help to design an optimized tissue-sectioning protocol. CONCLUSIONS: Micro-CT reference data help to identify where and to what extent tissue was lost or damaged during slide production, provides valuable anatomical information for reconstructing missing parts of a 3D tissue model, and aids in correcting reconstruction errors when fitting the image information in vivo and ex vivo.

Staining correction in digital pathology by utilizing a dye amount table.

The stained colors of the tissue components are popularly used as features for image analysis. However, variations in the staining condition of the histology slides prompt variations to the color distribution of the stained tissue samples which could impact the accuracy of the analysis. In this paper, we present a method to correct the staining condition of a histology image. In the method, a look-up table (LUT) based on the dye amounts absorbed by the sample is built. The LUT can be built when either (i) the source and reference staining conditions are specified or (ii) when the user simply wants to recreate his/her preferred staining condition without specifying any reference slide. The effectiveness of the present method was evaluated in two aspects: (i) CIELAB color difference of nuclei, cytoplasm, and red blood cells, between the ten different slides of liver tissue, and (ii) classification of the different tissue components. Application of the present staining correction method reduced the color difference between the slides by an average factor of 9.8 and the classification performance of a linear discriminant classifier improved by 16.5% on the average. Results of the paired t test statistical analysis further showed that the reduction in the CIELAB color difference between the slides and the improvement in the classifier's performance when staining correction was implemented is significant at p < 0.001.

Color standardization in whole slide imaging using a color calibration slide.

BACKGROUND: Color consistency in histology images is still an issue in digital pathology. Different imaging systems reproduced the colors of a histological slide differently. MATERIALS AND METHODS: Color correction was implemented using the color information of the nine color patches of a color calibration slide. The inherent spectral colors of these patches along with their scanned colors were used to derive a color correction matrix whose coefficients were used to convert the pixels' colors to their target colors. RESULTS: There was a significant reduction in the CIELAB color difference, between images of the same H & E histological slide produced by two different whole slide scanners by 3.42 units, P < 0.001 at 95% confidence level. CONCLUSION: Color variations in histological images brought about by whole slide scanning can be effectively normalized with the use of the color calibration slide.

Multispectral enhancement method to increase the visual differences of tissue structures in stained histopathology images.

In this paper we proposed a multispectral enhancement scheme in which the spectral colors of the stained tissue-structure of interest and its background can be independently modified by the user to further improve their visualization and color discrimination. The colors of the background objects are modified by transforming their N-band spectra through an NxN transformation matrix, which is derived by mapping the representative samples of their original spectra to the spectra of their target colors using least mean square method. On the other hand, the color of the tissue structure of interest is modified by modulating the transformed spectra with the sum of the pixel's spectral residual-errors at specific bands weighted through an NxN weighting matrix; the spectral error is derived by taking the difference between the pixel's original spectrum and its reconstructed spectrum using the first M dominant principal component vectors in principal component analysis. Promising results were obtained on the visualization of the collagen fiber and the non-collagen tissue structures, e.g., nuclei, cytoplasm and red blood cells (RBC), in a hematoxylin and eosin (H&E) stained image.

Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance.

Hematoxylin and eosin (H&E) stain is currently the most popular for routine histopathology staining. Special and/or immuno-histochemical (IHC) staining is often requested to further corroborate the initial diagnosis on H&E stained tissue sections. Digital simulation of staining (or digital staining) can be a very valuable tool to produce the desired stained images from the H&E stained tissue sections instantaneously. We present an approach to digital staining of histopathology multispectral images by combining the effects of spectral enhancement and spectral transformation. Spectral enhancement is accomplished by shifting the N-band original spectrum of the multispectral pixel with the weighted difference between the pixel's original and estimated spectrum; the spectrum is estimated using M < N principal component (PC) vectors. The pixel's enhanced spectrum is transformed to the spectral configuration associated to its reaction to a specific stain by utilizing an N × N transformation matrix, which is derived through application of least mean squares method to the enhanced and target spectral transmittance samples of the different tissue components found in the image. Results of our experiments on the digital conversion of an H&E stained multispectral image to its Masson's trichrome stained equivalent show the viability of the method.

Referenceless image quality evaluation for whole slide imaging.

OBJECTIVE: The image quality in whole slide imaging (WSI) is one of the most important issues for the practical use of WSI scanners. In this paper, we proposed an image quality evaluation method for scanned slide images in which no reference image is required. METHODS: While most of the conventional methods for no-reference evaluation only deal with one image degradation at a time, the proposed method is capable of assessing both blur and noise by using an evaluation index which is calculated using the sharpness and noise information of the images in a given training data set by linear regression analysis. The linear regression coefficients can be determined in two ways depending on the purpose of the evaluation. For objective quality evaluation, the coefficients are determined using a reference image with mean square error as the objective value in the analysis. On the other hand, for subjective quality evaluation, the subjective scores given by human observers are used as the objective values in the analysis. The predictive linear regression models for the objective and subjective image quality evaluations, which were constructed using training images, were then used on test data wherein the calculated objective values are construed as the evaluation indices. RESULTS: The results of our experiments confirmed the effectiveness of the proposed image quality evaluation method in both objective and subjective image quality measurements. Finally, we demonstrated the application of the proposed evaluation method to the WSI image quality assessment and automatic rescanning in the WSI scanner.

Multispectral enhancement towards digital staining.

BACKGROUND: Digital staining can be considered as a special form of image enhancement wherein the concern is not only to increase the contrast between the background objects and objects of interest, but to also impart the colors that mark the objects' unique reactions to a specific stain. In this paper, we extended the previously proposed multispectral enhancement methods such that the colors of the background pixels can also be changed. METHODS: In the previous multispectral enhancement methods a shifting factor is provided to the original spectrum. To implement digital staining, a spectral transformation process is introduced prior to spectral shifting. RESULTS: The enhancement method is applied to multispectral images of H&E stained liver tissue. The resulting digitally stained images show good correlation with the serial-section images of the tissue which are physically stained with Masson's trichrome. CONCLUSIONS: We have presented a multispectral enhancement method that can be adjusted to produce digitally stained-images. The current experimental results show the viability of the method. However, to achieve robust enhancement performance issues that arise from variations in staining conditions has to be addressed as well. This would be part of our future work.

Balancing image quality and compression factor for special stains whole slide images.

UNLABELLED: The objective is to find a practical balance between quality and performance for daily high volume whole slide imaging. We evaluated whole slide images created by various scanners at different compression factors to determine the best suitable quality factor (QF) needed for pathological images of special stains. METHOD: We scanned two sets of eight special stains slides each at 0.50 µm/pixel resolution in Hamamatsu scanner at six and five QF levels respectively to generate 72 images which were observed at a calibrated monitor by imaging specialists, a histo-technician, and a pathologist to find the most suitable QF level for special stains in digital slides. RESULTS: Most special stains images were acceptable at QF 30 except for the stain Reticulin where the lowest acceptable QF was 50. The compression of images from QF 90 to QF 50 reduced the size of the images by 62.73%. CONCLUSION: 0.50 µm/pixel images at QF 50 or above were found suitable 12 special stain.

Multispectral image enhancement for effective visualization.

Color enhancement of multispectral images is useful to visualize the image's spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.

Localization of eosinophilic esophagitis from H&E stained images using multispectral imaging.

This study is an initial investigation on the capability of multispectral imaging to capture subtle spectral information that would enable the automatic delineation between the eosinophilic esophagitis and other eosin stained tissue components, especially the RBCs. In the method, a principal component analysis (PCA) was performed on the spectral transmittance samples of the different tissue components, excluding however the transmittance samples of the eosinophilic esophagitis. From the average spectral error configuration of the eosinophilic esophagitis transmittance samples, i.e. the difference between the actual transmittance and the estimated transmittance using m PC vectors, we indentified two spectral bands by which we can localize the eosinophils. Initial results show the possibility of automatically localizing the eosinophilic esophagitis by utilizing spectral information.

Digital staining for histopathology multispectral images by the combined application of spectral enhancement and spectral transformation.

In this paper we introduced a digital staining method for histopathology images captured with an n-band multispectral camera. The method consisted of two major processes: enhancement of the original spectral transmittance and the transformation of the enhanced transmittance to its target spectral configuration. Enhancement is accomplished by shifting the original transmittance with the scaled difference between the original transmittance and the transmittance estimated with m dominant principal component (PC) vectors;the m-PC vectors were determined from the transmittance samples of the background image. Transformation of the enhanced transmittance to the target spectral configuration was done using an nxn transformation matrix, which was derived by applying a least square method to the enhanced and target spectral training data samples of the different tissue components. Experimental results on the digital conversion of a hematoxylin and eosin (H&E) stained multispectral image to its Masson's trichrome stained (MT) equivalent shows the viability of the method.

Improving the visualization and detection of tissue folds in whole slide images through color enhancement.

OBJECTIVE: The objective of this paper is to improve the visualization and detection of tissue folds, which are prominent among tissue slides, from the pre-scan image of a whole slide image by introducing a color enhancement method that enables the differentiation between fold and non-fold image pixels. METHOD: The weighted difference between the color saturation and luminance of the image pixels is used as shifting factor to the original RGB color of the image. RESULTS: Application of the enhancement method to hematoxylin and eosin (H&E) stained images improves the visualization of tissue folds regardless of the colorimetric variations in the images. Detection of tissue folds after application of the enhancement also improves but the presence of nuclei, which are also stained dark like the folds, was found to sometimes affect the detection accuracy. CONCLUSION: The presence of tissue artifacts could affect the quality of whole slide images, especially that whole slide scanners select the focus points from the pre-scan image wherein the artifacts are indistinguishable from real tissue area. We have a presented in this paper an enhancement scheme that improves the visualization and detection of tissue folds from pre-scan images. Since the method works on the simulated pre-scan images its integration to the actual whole slide imaging process should also be possible.

Detection of tissue folds in whole slide images.

In whole slide imaging (WSI) the quality of scanned images is an interplay between the hardware specifications of the scanning device and the condition of the tissue slide itself. Tissue artifacts such as folds and bubbles have been known to affect the efficiency of a whole slide scanning system in selecting the focus points wherein the presence of the said artifacts have been found to produce blur or unfocused images. Thus, for a whole slide scanning device to produce the best image quality, even with the presence of tissue artifacts, information on the location of these artifacts should be known such that they can be avoided in the selection of the focus points. In this paper we introduced an enhancement method to emphasize and detect the location of the tissue folds from whole slide images. Results of the experiments that we conducted on various H&E stained images that were scanned using different scanners show the robustness of the method to detect tissue folds.

Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance.

In this study, the digital transformation (digital staining) of the 16-band multispectral image of a hematoxylin and eosin (HE) stained pathological specimen to its Masson's trichrome (MT) stained counterpart is addressed. The digital staining procedure involves the classification of the various H&E-stained tissue components and then the transformation of their transmittance spectra to their equivalent MT-stained transmittance configurations. Combination of transmittance classifiers were designed to classify the various tissue components found in the multispectral images of an HE-stained specimen, e.g. nucleus, cytoplasm, red blood cell (RBC), fibrosis, etc.; while pseudo-inverse method was used to obtain the transformation matrices that would translate the transmittance spectra of the classified HE-stained multispectral pixels to their MT-stained configurations. To generate the digitally stained image, weighting factors, which were based on the classifiers beliefs, were introduced to the generated transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) to implement a digital staining framework in the pathological context.