Detection of Prostate Cancer from Whole-Mount Histology Images Using Markov Random Fields

Annually in the US 186, 000 men are diagnosed with prostate cancer (CaP) and over43, 000 die from it. The analysis of whole-mount histological sections (WMHSs) is needed to help determine treatment following prostatectomy and to create the “ground truths” of CaP spatial extent required to evaluate other diagnostic modalities (eg. magnetic resonance imaging). Computer aided diagnosis (CAD) of WMHSs could increase analysis throughput and offer a means for identifying image based biomarkers capable of distinguishing, for example, CaP progressors from non-progressors. In this paper we introducea CAD algorithm for detecting CaP in low-resolution WMHSs. At low-resolution the prominent visible structures are glands. Since cancerous and benign glands differ in size, gland area provides a discriminative feature. Additionally, cancerous glands tend to be near other cancerous glands. This information is modeled using Markov random fields (MRFs). However, unlike most MRF strategies which rely on heuristic formulations, we introduce a novel methodology that allows the MRF to be modeled directly from training data. Our CAD system identifies cancerous regions with a sensitivity of 0.8670 and a specificity of0.9524. I. I NTRODUCTION Annually in the US186, 000 people are diagnosed with prostate cancer (CaP) and over 43, 000 die from it. The examination of histological specimens remains the definiti ve test for diagnosing CaP. Though the majority of such analysi s is performed on core biopsies, the consideration of wholemount histological sections (WMHSs) is also important. Following prostatectomy, the staging and grading of WMHSs help determine prognosis and treatment. Additionally, the spat ial extent of CaP as established by the analysis of WMHSs can be registered to other modalities (eg. magnetic resonan ce imaging), providing a “ground truth” for evaluation. The development of computer aided diagnosis (CAD) algorithms for WMHSs is also significant: 1) CAD offers a viable means for analyzing the vast amount of the data present in WMHSs, a time-consuming task currently performed by pathologists , 2) the consistent, quantified features and results inherent to CAD systems can be used to refine our own understanding This work was made possible due to grants from the Wallace H. Co ulter foundation, New Jersey Commission on Cancer Research, the Nat ional Cancer Institute (R21CA127186-01, R03CA128081-01), the Society for Imaging and Informatics on Medicine, and the Life Science Commercializati on Award. Corresponding authors: James Monaco, email: jpmonaco@rci.ru tgers.edu, Anant Madabhushi email: anantm@rci.rutgers.edu of prostate histology, thereby helping doctors improve per formance and reduce variability in grading and detection, and 3) the data mining of quantified morphometric features may provide means for biomarker discovery, enabling for exampl e, the discrimination of CaP progressors from non-progressor s. With respect to prostate histology Begelman [1] considered nuclei segmentation for hematoxylin and eosin (H&E) staine d prostate tissue samples. In [2] Naik used features derived f rom the segmentation of nuclei and glands to determine Gleason grade in core biopsy samples. To aid in manual cancer diagnosis Gao [3] applied histogram thresholding to enhance the appearance of cytoplasm and nuclei. In this paper we introdu ce the first CAD system for detecting CaP in WMHSs. This system is specifically designed to operate at low-resolutio n (0.01mm per pixel) and will eventually constitute the initial stage of a hierarchical analysis algorithm, identifying ar eas for which a higher-resolution examination is necessary. As substantiated in our previous approach [4] for prostate bio psy specimens, a hierarchical methodology provides an effecti ve means for dealing with high density data (prostate WMHSs have 500 times the amount of data compared to a four view mammogram). Even at low resolutions, gland size and morphology are noticeably different in cancerous and benig n regions [5]. In fact, we will demonstrate that gland size alo ne is a sufficient feature for yielding an accurate and efficient algorithm. Additionally, we leverage the fact that cancero us glands tend to be proximate to other cancerous glands. This information is modeled using Markov random fields (MRFs). However, unlike the preponderance of MRF strategies (such as the Potts model) which rely on heuristic formulations, we introduce a novel methodology that allows the MRF to be modeled directly from training data. Our CAD algorithm proceeds as follows: 1) gland segmentation is performed on the luminance channel of a color H&E stained WMHS, producing gland boundaries, 2) the system then calculates morphological features for each gland, 3) t he features are classified, labeling the glands as either malig nant or benign, 4) the labels serve as the starting point for the MRF iteration which then produces the final labeling, and 5) the cancerous glands are consolidated into regions. Sectio n II considers these steps in detail. In Section III we discuss th e results of applying our algorithm to WMHSs. In Section IV we present our concluding remarks. Fig. 1. Example of current segmented region (CR), internal bou ndary (IB), and current boundary (CB) during a step of the region growing algorithm. II. M ETHODOLOGY A. Gland Segmentation In the luminance channel of histological images glands appear as regions of contiguous, high intensity pixels circ umscribed by sharp, pronounced boundaries. To segment these regions we adopt a routine first used for segmenting breast microcalcifications [6]. We briefly outline this algorithm. First define the following: 1)current region(CR) is the set of pixels representing the segmented region in the current step of the algorithm, 2)current boundary(CB) is the set of pixels that neighbor CR in an 8-connected sense, but are not in CR, and 3) internal boundary(IB) is the subset of pixels in CR that neighbor CB. These definitions are illustrated in Figure 1. T he growing procedure begins by initializing CR to a seed pixel assumed to lie within the gland. At each iteration CR expands by aggregating the pixel in CB with the greatest intensity. C R and CB are updated, and the process continues. The algorithm terminates when theL∞ norm from the seed to the next aggregated pixel exceeds a predetermined threshold. That i s, the L∞ norm establishes a square bounding box about the seed; the growing procedure terminates when the algorithm attempts to add a pixel outside this box. During each iterati on the algorithm measures the boundary strength which is define d as the average intensity of the pixels in IB minus the average intensity of the pixels in CB. After the growing procedure terminates, the region with the greatest boundary strength is selected. Seed pixels are established by finding peaks in the image after Gaussian smoothing. Since gland sizes can vary greatly, we smooth at multiple scales, each of which is defined by the sigmaσg ∈ {0.2, 0.1, 0.05, 0.025}mm of a Gaussian kernel. 1 The lengthl of each side of the bounding box used for terminating the segmentation step is tied to the scale: l = 12σg. The final segmented regions may overlap. In this event the region with the highest boundary measure is retained. Figures 2(a) and 2(g) are H&E stained WMHSs with black ink marks providing a rough truth (RT) of CaP extent. Gland segmentation results are shown in Figures 2(b ) and 2(h). Figures 2(c) and 2(i) provide magnified views of the regions of interest in Figures 2(b) and 2(h). The centroi ds of glands whose probability of malignancy exceeds ρ = 0.15 are marked with green dots in Figures 2(d) and 2(j) 2. This labeling is refined by the MRF iteration, producing the gland 1The growing procedure operates on the original image. 2Figures 2(d), 2(j), 2(e), 2(k), 2(f) and 2(l) will be further xplained in later sections. centroids shown in Figures 2(e) and 2(k). Figures 2(f) and 2( l) show the aggregation of cancerous glands into regions (gree n) along with a high-fidelity truth (HFT) of CaP extent (yellow) . B. Feature Extraction, Modeling, and Bayesian Classificati on Gland area is used to discriminate benign from malignant glands. Since we employ a Bayesian framework, we require estimates of the conditional probability density functions ( pdfs) of gland area for both malignant ωm and benignωb glands. Using the equivalent square root of gland area (SRGA), the pdfs f (