Local Morphologic Scale: Application to Segmenting Tumor Infiltrating Lymphocytes in Ovarian Cancer TMAs Andrew Janowczyk a,b , Sharat Chandran b , Michael Feldman c , Anant Madabhushi a a Department of Biomedical Engineering, Rutgers University, USA; b Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India; c Department of Pathology and Lab Medicine, Hospital at the University of Pennsylvania, USA ABSTRACT 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 descrip- tors 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. Keywords: Local Morphologic Scale, Tumor Infiltrating Lymphocytes (TIL), Locally Adaptive Scale, Digital Pathology, Segmentation, Classification 1. INTRODUCTION The notion of scale in the context of image processing has been routinely employed over the last few decades to facilitate multi-resolution feature analysis; the assumption being that certain pertinent image features are only discernible at certain image scales and hence a spectrum of image resolutions needs to be considered for object recognition. Multi-scale approaches (scale-space 1 and hierarchical pyramids 2 ) envisioned image processing operations being applied on a single image at varying levels of resolution; homogeneous regions being operated on at a lower resolution, with more heterogeneous regions being examined at higher resolutions. A limitation of Email Addresses: andrew@cse.iitb.ac.in (Andrew Janowczyk), sharat@cse.iitb.ac.in (Sharat Chandran), anantm@rci.rutgers.edu (Anant Madabhushi) Medical Imaging 2011: Image Processing, edited by Benoit M. Dawant, David R. Haynor, Proc. of SPIE Vol. 7962, 79622N · © 2011 SPIE · CCC code: 1605-7422/11/$18 · doi: 10.1117/12.878415 Proc. of SPIE Vol. 7962 79622N-1 Downloaded from SPIE Digital Library on 21 May 2011 to 198.151.130.3. Terms of Use: http://spiedl.org/terms