A Biologically-inspired algorithm for the segmentation of cell nuclei in high resolution histological images

Immunohistochemistry (IHC) images are of high resolution and are stained for ER, PR, KI-67 and p53. Image processing can serve an important role in the diagnosis of disease from histopathological data due to its ability to process and analyze whole-slide digital images. Most of the traditional algorithms do not perform segmentation at a low visual level as the spatial relationship between pixels is not often entirely utilized. We developed an algorithm designed to mimic the visual system that utilizes a set of image features and identifies discontinuities within each feature domain. These features are further combined using a concept in neuroscience to generate an intermediate image that is more amenable to traditional tools for performing nuclear segmentation

Laboratory computer performance in a digital pathology environment: Outcomes from a single institution

Background: In an effort to provide improved user experience and system reliability at a moderate cost, our department embarked on targeted upgrades of a total of 87 computers over a period of 3 years. Upgrades came in three forms: (i) replacement of the computer with newer architecture, (ii) replacement of the computer's hard drive with a solid-state drive (SSD), or (iii) replacement of the computer with newer architecture and a SSD. Methods: We measured the impact of each form of upgrade on a set of pathology-relevant tasks that fell into three categories: standard use, whole-slide navigation, and whole-slide analysis. We used time to completion of a task as the primary variable of interest. Results: We found that for most tasks, the SSD upgrade had a greater impact than the upgrade in architecture. This effect was especially prominent for whole-slide viewing, likely due to the way in which most whole-slide viewers cached image tiles. However, other tasks, such as whole-slide image analysis, often relied less on disk input or output and were instead more sensitive to the computer architecture. Conclusions: Based on our experience, we suggest that SSD upgrades are viewed in some settings as a viable alternative to complete computer replacement and recommend that computer replacements in a digital pathology setting are accompanied by an upgrade to SSDs

An alternative reference space for H&E color normalization.

Digital imaging of H&E stained slides has enabled the application of image processing to support pathology workflows. Potential applications include computer-aided diagnostics, advanced quantification tools, and innovative visualization platforms. However, the intrinsic variability of biological tissue and the vast differences in tissue preparation protocols often lead to significant image variability that can hamper the effectiveness of these computational tools. We developed an alternative representation for H&E images that operates within a space that is more amenable to many of these image processing tools. The algorithm to derive this representation operates by exploiting the correlation between color and the spatial properties of the biological structures present in most H&E images. In this way, images are transformed into a structure-centric space in which images are segregated into tissue structure channels. We demonstrate that this framework can be extended to achieve color normalization, effectively reducing inter-slide variability

An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides

Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing

Modulation by individual pixel values preserves image detail.

<p>(A) The normalized mutual information (NMI) refers to the ratio of the mutual information between normalized and unnormalized images and the mutual information between the unnormalized image and itself. This ratio was measured for each color channel individually (dark bars). As a control, the same metric was applied to compare an image projected to the same normalized space without modulation about the mean to the unnormalized image. In this way, only the pixel’s class (e.g. nuclei, stroma) contributed to the prediction of the original image. (B) The NMI was computed for noise-added images and its value depended on the amount of noise added, represented by σ. Measurements were made separately along the Value channel (Val) and Hue-saturation plane (Hue-Sat). (C) Representative unnormalized images for one case are shown after different amounts of noise were added corresponding to <i>NMI = 1</i> (left), <i>NMI = 0</i>.<i>5</i> (middle), and <i>NMI = 0</i>.<i>15</i> (right). For visualization purposes, images were saturated to account for pixel values out of range. This did not contribute to the measurements shown in (A) or (B).</p

Color normalization for visualization.

<p>(A,B) Two images from the UCSB data set exhibited different staining properties. A strong red hue was observed in (A) while the image in (B) had a greater proportion of blue. (C) Likewise, a representative image from the Drexel breast cancer databank exhibited yet a different color property. (D-F) Color normalization produced visually similar color properties.</p