Classification of Histologic Images Using a Single Staining: Experiments with Deep Learning on Deconvolved Images.

The automated analysis of digitized immunohistochemistry microscope slides is usually a challenging task, because markers should be analysed on the tumor area only. Tumor areas could be recognized on a different slide, stained with Haematoxylin-Eosin. The basic idea of the present poster is to evaluate how well deep learning methods perform on the single haematoxylin component of staining, with the prospective possibility of developing a classifier able to recognize tumor areas on IHC slides on their haematoxylin component only. In a preliminary experiment, single stain images obtained by H-E color deconvolution showed an accuracy of 0.808 and 0.812 for Hematoxilyn and Eosin components, respectively.

Expression and modulation of S100A4 protein by human mast cells.

S100A4 protein is expressed in fibroblasts during tissue remodelling and in cancer stem cells and it induces the metastatic spread of tumor cells. In mast cells (MCs) S100A4 have been found in some pathological conditions, but its function in normal MCs remains to be described. The purpose of this study was to characterize the cellular localization of the S100A4 protein in MCs of human tissues with inflammatory or tumor disorders and, to determine the consequence of reducing its expression in MC response. We found that tissue resident MCs stained positive to S100A4. Both human HMC-1 cell line and resting CD34+-derived MCs expressed S100A4, whose levels were differentially modulated upon MC activation. Downregulation of the S100A4 protein resulted in MC growth inhibition, enhanced apoptosis and deregulation of MMP-1 and MMP-10 production. Our results suggest that S100A4 is also playing a role in the MC life cycle and functions.

SlideJ: An ImageJ plugin for automated processing of whole slide images.

The digital slide, or Whole Slide Image, is a digital image, acquired with specific scanners, that represents a complete tissue sample or cytological specimen at microscopic level. While Whole Slide image analysis is recognized among the most interesting opportunities, the typical size of such images-up to Gpixels- can be very demanding in terms of memory requirements. Thus, while algorithms and tools for processing and analysis of single microscopic field images are available, Whole Slide images size makes the direct use of such tools prohibitive or impossible. In this work a plugin for ImageJ, named SlideJ, is proposed with the objective to seamlessly extend the application of image analysis algorithms implemented in ImageJ for single microscopic field images to a whole digital slide analysis. The plugin has been complemented by examples of macro in the ImageJ scripting language to demonstrate its use in concrete situations.

An adaptive positivity thresholding method for automated Ki67 hotspot detection (AKHoD) in breast cancer biopsies.

The proliferative activity of breast cancer tissue can be estimated using the Ki67 biomarker. The percentage of positivity of such biomarker is correlated with proliferation and consequently with the prognosis of a breast tumor. Ki67 marked tissue samples are analyzed by an experienced pathologist who identifies the most active areas of tumor cell proliferation called hotspots, and estimates the positivity of each case. A method for the Automated Ki67 Hotspot Detection (AKHoD) is presented in this work. The main objective of the AKHoD method is to automatically and efficiently provide the pathologist with suggestions about Ki67 hotspot areas as a decision support. The input of AKHoD is a digital slide that is divided in tiles. For each tile, AKHoD provides a rough estimate of positivity and cellularity, summarized in very low resolution positivity and cellularity images. In a second step, an adaptive thresholding is applied to such positivity image to identify the most positive connected and convex areas, within cellularity limits set by current guidelines (that is, 500-2000). The method has been preliminarily validated on 50 digital slides for which three expert pathologists provided gold standard hotspots. 82% of the gold standard hotspots have been successfully recognized by the system, spending an average of 54s per slide. While further validation is needed taking into account also patients follow-up, this first experimentation suggests that the proposed method could be adequate for supporting the pathologist in hotspot detection.

Prospective MR image alignment between breath-holds: Application to renal BOLD MRI.

PURPOSE: To present an image registration method for renal blood oxygen level-dependent (BOLD) measurements that enables semiautomatic assessment of parenchymal and medullary R2* changes under a functional challenge. METHODS: In a series of breath-hold acquisitions, three-dimensional data were acquired initially for prospective image registration of subsequent BOLD measurements. An algorithm for kidney alignment for BOLD renal imaging (KALIBRI) was implemented to detect the positions of the left and right kidney so that the kidneys were acquired in the subsequent BOLD measurement at consistent anatomical locations. Residual in-plane distortions were corrected retrospectively so that semiautomatic dynamic R2* measurements of the renal cortex and medulla become feasible. KALIBRI was tested in six healthy volunteers during a series of BOLD experiments, which included a 600- to 1000-mL water challenge. RESULTS: Prospective image registration and BOLD imaging of each kidney was achieved within a total measurement time of about 17 s, enabling its execution within a single breath-hold. KALIBRI improved the registration by up to 35% as found with mutual information measures. In four volunteers, a medullary R2* decrease of up to 40% was observed after water ingestion. CONCLUSION: KALIBRI improves the quality of two-dimensional time-resolved renal BOLD MRI by aligning local renal anatomy, which allows for consistent R2* measurements over many breath-holds. Magn Reson Med 77:1573-1582, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Non-Parametric Bayesian Registration (NParBR) of Body Tumors in DCE-MRI Data.

The identification of tumors in the internal organs of chest, abdomen, and pelvis anatomic regions can be performed with the analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data. The contrast agent is accumulated differently by pathologic and healthy tissues and that results in a temporally varying contrast in an image series. The internal organs are also subject to potentially extensive movements mainly due to breathing, heart beat, and peristalsis. This contributes to making the analysis of DCE-MRI datasets challenging as well as time consuming. To address this problem we propose a novel pairwise non-rigid registration method with a Non-Parametric Bayesian Registration (NParBR) formulation. The NParBR method uses a Bayesian formulation that assumes a model for the effect of the distortion on the joint intensity statistics, a non-parametric prior for the restored statistics, and also applies a spatial regularization for the estimated registration with Gaussian filtering. A minimally biased intra-dataset atlas is computed for each dataset and used as reference for the registration of the time series. The time series registration method has been tested with 20 datasets of liver, lungs, intestines, and prostate. It has been compared to the B-Splines and to the SyN methods with results that demonstrate that the proposed method improves both accuracy and efficiency.

Bi-modal Non-rigid Registration of Brain MRI Data with Deconvolution of Joint Statistics.

A brain MRI protocol typically includes several imaging contrasts that can provide complementary information by highlighting different tissue properties. The acquired datasets often need to be co-registered or placed in a standard anatomic space before any further processing. Current registration methods particularly for multicontrast data are computationally very intensive, their resolution is lower than that of the images, and their distance metric and its optimization can be limiting. In this work a novel and effective non-rigid registration method is proposed that is based on the restoration of the joint statistics of pairs of such images. The registration is performed with the deconvolution of the joint statistics with an adaptive Wiener filter. The deconvolved statistics are forced back to the spatial domain to estimate a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared with the B-Splines method implemented in 3DSlicer and with the SyN method implemented in the ANTs toolkit. The validation has been performed with a simulated Shepp-Logan phantom, a BrainWeb phantom, the real data of the NIREP database, and real multi-contrast datasets of healthy volunteers. The proposed method has shown improved comparative accuracy as well as analytical efficiency.

Registration of abdominal tumor DCE-MRI data based on deconvolution of joint statistics.

The analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data of body tumors presents several challenges. The accumulation of contrast agent in tissues results in a temporally varying contrast in an image series. At the same time, the body regions are subject to potentially extensive motion mainly due to breathing, heart beat, and peristalsis. This complicates any further automated analysis of a DCE-MRI time series such as for tumor lesion segmentation and volumetry. To address this problem we propose a novel effective non-rigid registration method based on the restoration of the joint statistics of pairs of images in the time series. Every image in the time series is registered to a reference one from the contrast enhanced phase. The pairwise registration is performed with deconvolution of the joint statistics, forcing the results back to the spatial domain and regularizing them with Gaussian spatial smoothing. The registration method has been validated with both a simulated phantom as well as real datasets with improved results for both its accuracy and efficiency.