Whole tumor section quantitative image analysis maximizes between-pathologists' reproducibility for clinical immunohistochemistry-based biomarkers.

Pathologists have had increasing responsibility for quantitating immunohistochemistry (IHC) biomarkers with the expectation of high between-reader reproducibility due to clinical decision-making especially for patient therapy. Digital imaging-based quantitation of IHC clinical slides offers a potential aid for improvement; however, its clinical adoption is limited potentially due to a conventional field-of-view annotation approach. In this study, we implemented a novel solely morphology-based whole tumor section annotation strategy to maximize image analysis quantitation results between readers. We first compare the field-of-view image analysis annotation approach to digital and manual-based modalities across multiple clinical studies (~120 cases per study) and biomarkers (ER, PR, HER2, Ki-67, and p53 IHC) and then compare a subset of the same cases (~40 cases each from the ER, PR, HER2, and Ki-67 studies) using whole tumor section annotation approach to understand incremental value of all modalities. Between-reader results for each biomarker in relation to conventional scoring modalities showed similar concordance as manual read: ER field-of-view image analysis: 95.3% (95% CI 92.0-98.2%) vs digital read: 92.0% (87.8-95.8%) vs manual read: 94.9% (91.4-97.8%); PR field-of-view image analysis: 94.1% (90.3-97.2%) vs digital read: 94.0% (90.2-97.1%) vs manual read: 94.4% (90.9-97.2%); Ki-67 field-of-view image analysis: 86.8% (82.1-91.4%) vs digital read: 76.6% (70.9-82.2%) vs manual read: 85.6% (80.4-90.4%); p53 field-of-view image analysis: 81.7% (76.4-86.8%) vs digital read: 80.6% (75.0-86.0%) vs manual read: 78.8% (72.2-83.3%); and HER2 field-of-view image analysis: 93.8% (90.0-97.2%) vs digital read: 91.0 (86.6-94.9%) vs manual read: 87.2% (82.1-91.9%). Subset implementation and analysis on the same cases using whole tumor section image analysis approach showed significant improvement between pathologists over field-of-view image analysis and manual read (HER2 100% (97-100%), P=0.013 field-of-view image analysis and 0.013 manual read; Ki-67 100% (96.9-100%), P=0.040 and 0.012; ER 98.3% (94.1-99.5%), p=0.232 and 0.181; and PR 96.6% (91.5-98.7%), p=0.012 and 0.257). Overall, whole tumor section image analysis significantly improves between-pathologist's reproducibility and is the optimal approach for clinical-based image analysis algorithms.

Computerized classification of intraductal breast lesions using histopathological images.

In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250,000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus, improvement in the diagnostic reproducibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% is obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in improving diagnostic accuracy and reproducibility when used as a "second reader" in conjunction with the pathologists.

Microscopic image analysis for quantitative characterization of muscle fiber type composition.

Skeletal muscles consist of muscle fibers that are responsible for contracting and generating force. Skeletal muscle fibers are categorized into distinct subtypes based on several characteristics such as contraction time, force production and resistance to fatigue. The composition of distinct muscle fibers in terms of their number and cross-sectional areas is characterized by a histological examination. However, manual delineation of individual muscle fibers from digitized muscle histology tissue sections is extremely time-consuming. In this study, we propose an automated image analysis system for quantitative characterization of muscle fiber type composition. The proposed system operates on digitized histological muscle tissue slides and consists of the following steps: segmentation of muscle fibers, registration of successive slides with distinct stains, and classification of muscle fibers into distinct subtypes. The performance of the proposed approach was tested on a dataset consisting of 25 image pairs of successive muscle histological cross-sections with different ATPase stain. Experimental results demonstrate a promising overall segmentation and classification accuracy of 89.1% in identifying muscle fibers of distinct subtypes.

Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation.

Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL has a variable clinical course, and important clinical treatment decisions for FL patients are based on histological grading, which is done by manually counting the large malignant cells called centroblasts (CB) in ten standard microscopic high-power fields from H&E-stained tissue sections. This method is tedious and subjective; as a result, suffers from considerable inter and intrareader variability even when used by expert pathologists. In this paper, we present a computer-aided detection system for automated identification of CB cells from H&E-stained FL tissue samples. The proposed system uses a unitone conversion to obtain a single-channel image that has the highest contrast. From the resulting image, which has a bimodal distribution due to the H&E stain, a cell-likelihood image is generated. Finally, a two-step CB detection procedure is applied. In the first step, we identify evident nonCB cells based on size and shape. In the second step, the CB detection is further refined by learning and utilizing the texture distribution of nonCB cells. We evaluated the proposed approach on 100 region-of-interest images extracted from ten distinct tissue samples and obtained a promising 80.7% detection accuracy.

Computer-aided prognosis of neuroblastoma: detection of mitosis and karyorrhexis cells in digitized histological images.

Histopathological examination is one of the most important steps in evaluating prognosis of patients with neuroblastoma (NB). NB is a pediatric tumor of sympathetic nervous system and current evaluation of NB tumor histology is done according to the International Neuroblastoma Pathology Classification. The number of cells undergoing either mitosis or karyorrhexis (MK) plays an important role in this classification system. However, manual counting of such cells is tedious and subject to considerable inter- and intra-reader variations. A computer-assisted system may allow more precise results leading to more accurate prognosis in clinical practice. In this study, we propose an image analysis approach that operates on digitized NB histology samples. Based on the likelihood functions estimated from the samples of manually marked regions, we compute the probability map that indicates how likely a pixel belongs to an MK cell. Component-wise 2-step thresholding of the generated probability map provides promising results in detecting MK cells with an average sensitivity of 81.1% and 12.2 false positive detections on average.

Extraction of color features in the spectral domain to recognize centroblasts in histopathology.

In this paper, we are proposing a novel automated method to recognize centroblast (CB) cells from non-centroblast (non-CB) cells for computer-assisted evaluation of follicular lymphoma tissue samples. The method is based on training and testing of a quadratic discriminant analysis (QDA) classifier. The novel aspects of this method are the identification of the CB object with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract color texture features. Both geometric and texture features are used to achieve the classification. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the performance of the system significantly. The implemented method can identify centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56%.

Stroma classification for neuroblastoma on graphics processors.

Neuroblastoma is one of the most common childhood cancers. We are developing an image analysis system to assist pathologists in their prognosis. Since this system operates on relatively large-scale images and requires sophisticated algorithms, computerised analysis takes a long time to execute. In this paper, we propose a novel approach to benefit from high memory bandwidth and strong floating-point capabilities of graphics processing units. The proposed approach achieves a promising classification accuracy of 99.4% and an execution performance with a gain factor up to 45 times compared to hand-optimised C++ code running on the CPU.

Feature-based registration of histopathology images with different stains: an application for computerized follicular lymphoma prognosis.

Follicular lymphoma (FL) is the second most common type of non-Hodgkin's lymphoma. Manual histological grading of FL is subject to remarkable inter- and intra-reader variations. A promising approach to grading is the development of a computer-assisted system that improves consistency and precision. Correlating information from adjacent slides with different stain types requires establishing spatial correspondences between the digitized section pair through a precise non-rigid image registration. However, the dissimilar appearances of the different stain types challenges existing registration methods. This study proposes a method for the automatic non-rigid registration of histological section images with different stain types. This method is based on matching high level features that are representative of small anatomical structures. This choice of feature provides a rich matching environment, but also results in a high mismatch probability. Matching confidence is increased by establishing local groups of coherent features through geometric reasoning. The proposed method is validated on a set of FL images representing different disease stages. Statistical analysis demonstrates that given a proper feature set the accuracy of automatic registration is comparable to manual registration.

Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification.

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.

Computerized pathological image analysis for neuroblastoma prognosis.

We present a pathological image analysis system for the computer-aided prognosis of neuroblastoma, a childhood cancer. The image analysis system automatically classifies Schwannian stromal development of pathological tissues and determines the grade of differentiation. Due to the demanding computational cost of processing large digitized slides, the system was implemented on a cluster of computers with automated load balancing within a multi-resolution framework. In our experiments, the overall accuracies for stromal classification and the grade of differentiation were 96.6% and 95.3%, respectively. Additionally, the multi-resolution framework reduced the run time of the single resolution approach by 53% and 34% on average for stromal classification and grade of differentiation, respectively. For these two cases, parallelization on a 16-node cluster reduced the sequential run time by 92% and 88% on average. Accuracy and efficiency of these techniques are promising for the development a computer-assisted neuroblastoma prognosis system.