Search

There is a need for identifying quantitative imaging (e.g. MRI) signatures for prostate cancer (CaP), so that computer-aided diagnostic methods can be trained to detect disease extent in vivo. Determining CaP extent on in vivo MRI is difficult to do; however, with the availability of ex vivo surgical whole mount histological sections (WMHS) for CaP patients undergoing radical prostatectomy, co-registration methods can be applied to align and map disease extent onto pre-operative MR imaging from the post-operative histology. Yet obtaining digitized images of WHMS for co-registration with the pre-operative MRI is cumbersome since (a) most digital slide scanners are unable to accommodate the entire section, and (b) significant technical expertise is required for whole mount slide preparation. Consequently, most centers opt to construct quartered sections of each histology slice. Prior to co-registration with MRI, however, these quartered sections need to be digitally stitched together to reconstitute a digital, pseudo WMHS. Histostitcher© is an interactive software program that uses semi-automatic registration tools to digitally stitch quartered sections into pseudo WMHS. Histostitcher© was originally developed using the GUI tools provided by the Matlab programming interface, but the clinical use was limited due to the inefficiency of the interface. The limitations of the Matlab based GUI include (a) an inability to edit the fiducials, (b) the rendering being extremely slow, and (c) lack of interactive and rapid visualization tools. In this work, Histostitcher© has been integrated into the eXtensible Imaging Platform (XIP TM) framework (a set of libraries containing functionalities for analyzing and visualizing medical image data). XIP TMlends the stitching tool much greater flexibility and functionality by (a) allowing interactive and seamless navigation through the full resolution histology images, (b) the ability to easily add, edit, or remove fiducials and annotations in order to register the quadrants and map the disease extent. In this work, we showcase examples of digital stitching of quartered histological sections into pseudo-WHMS using Histostitcher © via the new XIP TMinterface. This tool will be particularly useful in clinical trials and large cohort studies where a quick, interactive way of digitally reconstructing pseudo WMHS is required.

In this paper we present the concept and associated methodological framework for a novel locally adaptive scale notion called local morphological scale (LMS). Broadly speaking, the LMS at every spatial location is defined as the set of spatial locations, with associated morphological descriptors, which characterize the local structure or heterogeneity for the location under consideration. More specifically, the LMS is obtained as the union of all pixels in the polygon obtained by linking the final location of trajectories of particles emanating from the location under consideration, where the path traveled by originating particles is a function of the local gradients and heterogeneity that they encounter along the way. As these particles proceed on their trajectory away from the location under consideration, the velocity of each particle (i.e. do the particles stop, slow down, or simply continue around the object) is modeled using a physics based system. At some time point the particle velocity goes to zero (potentially on account of encountering (a) repeated obstructions, (b) an insurmountable image gradient, or (c) timing out) and comes to a halt. By using a Monte-Carlo sampling technique, LMS is efficiently determined through parallelized computations. LMS is different from previous local scale related formulations in that it is (a) not a locally connected sets of pixels satisfying some pre-defined intensity homogeneity criterion (generalized-scale), nor is it (b) constrained by any prior shape criterion (ball-scale, tensor-scale). Shape descriptors quantifying the morphology of the particle paths are used to define a tensor LMS signature associated with every spatial image location. These features include the number of object collisions per particle, average velocity of a particle, and the length of the individual particle paths. These features can be used in conjunction with a supervised classifier to correctly differentiate between two different object classes based on local structural properties. In this paper, we apply LMS to the specific problem of classifying regions of interest in Ovarian Cancer (OCa) histology images as either tumor or stroma. This approach is used to classify lymphocytes as either tumor infiltrating lymphocytes (TILs) or non-TILs; the presence of TILs having been identified as an important prognostic indicator for disease outcome in patients with OCa. We present preliminary results on the tumor/stroma classification of 11,000 randomly selected locations of interest, across 11 images obtained from 6 patient studies. Using a Probabilistic Boosting Tree (PBT), our supervised classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.8341 ±0.0059 over 5 runs of randomized cross validation. The average LMS computation time at every spatial location for an image patch comprising 2000 pixels with 24 particles at every location was only 18s.

An important criterion for identifying complicated objects with multiple attributes is the use of domain knowledge which reflects the precise spatial linking of the constituent attributes. Hence, simply detecting the presence of the low-level attributes that constitute the object, even in cases where these attributes might be detected in spatial proximity to each other is usually not a robust strategy. The O'Callaghan neighborhood is an ideal vehicle for characterizing objects comprised of multiple attributes spatially connected to each other in a precise fashion because it allows for modeling and imposing spatial distance and directional constraints on the object attributes. In this work we apply the O'Callaghan neighborhood to the problem of tubule identification on hematoxylin and eosin (H & E) stained breast cancer (BCa) histopathology, where a tubule is characterized by a central lumen surrounded by cytoplasm and a ring of nuclei around the cytoplasm. The detection of tubules is important because tubular density is an important predictor in cancer grade determination. In the context of ER+ BCa, grade has been shown to be strongly linked to disease aggressiveness and patient outcome. The more standard pattern recognition approaches to detection of complex objects typically involve training classifiers for low-level attributes individually. For tubule detection, the spatial proximity of lumen, cytoplasm, and nuclei might suggest the presence of a tubule. However such an approach could also suffer from false positive errors due to the presence of fat, stroma, and other lumen-like areas that could be mistaken for tubules. In this work, tubules are identified by imposing spatial and distance constraints using O'Callaghan neighborhoods between the ring of nuclei around each lumen. In this work, cancer nuclei in each image are found via a color deconvolution scheme, which isolates the hematoxylin stain, thereby enabling automated detection of individual cell nuclei. The potential lumen areas are segmented using a Hierarchical Normalized Cut (HNCut) initialized Color Gradient based Active Contour model (CGAC). The HNCut algorithm detects lumen-like areas within the image via pixel clustering across multiple image resolutions. The pixel clustering provides initial contours for the CGAC. From the initial contours, the CGAC evolves to segment the boundaries of the potential lumen areas. A set of 22 graph-based image features characterizing the spatial linking between the tubular attributes is extracted from the O'Callaghan neighborhood in order to distinguish true from false lumens. Evaluation on 1226 potential lumen areas from 14 patient studies produces an area under the receiver operating characteristic curve (AUC) of 0.91 along with the ability to classify true lumen with 86% accuracy. In comparison to manual grading of tubular density over 105 images, our method is able to distinguish histopathology images with low and high tubular density at 89% accuracy (AUC = 0.94).