Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors:a pilot study for future algorithmic approach

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Histopathology stain-color normalization using deep generative models

Performance of designed CAD algorithms for histopathology image analysis is affected by the amount of variations in the samples such as color and intensity of stained images. Stain-color normalization is a well-studied technique for compensating such effects at the input of CAD systems. In this paper, we introduce unsupervised generative neural networks for performing stain-color normalization. For color normalization in stained hematoxylin and eosin (H&E) images, we present three methods based on three frameworks for deep generative models: variational auto-encoder (VAE), generative adversarial networks (GAN) and deep convolutional Gaussian mixture models (DCGMM). Our contribution is defining the color normalization as a learning generative model that is able to generate various color copies of the input image through a nonlinear parametric transformation. In contrast to earlier generative models proposed for stain-color normalization, our approach does not need any labels for data or any other assumptions about the H&E image content. Furthermore, our models learn a parametric transformation during training and can convert the color information of an input image to resemble any arbitrary reference image. This property is essential in time-critical CAD systems in case of changing the reference image, since our approach does not need retraining in contrast to other proposed generative models for stain-color normalization. Experiments on histopathological H&E images with high staining variations, collected from different laboratories, show that our proposed models outperform quantitatively state-of-the-art methods in the measure of color constancy with at least 10-15%, while the converted images are visually in agreement with this performance improvement

Successful Combination of Sunitinib and Girentuximab in Two Renal Cell Carcinoma Animal Models: A Rationale for Combination Treatment of Patients with Advanced RCC

Anti-angiogenic treatment with tyrosine kinase inhibitors (TKI) has lead to an impressive increase in progression-free survival for patients with metastatic RCC (mRCC), but mRCC remains largely incurable. We combined sunitinib, targeting the endothelial cells with Girentuximab (monoclonal antibody cG250, recognizing carbonic anhydrase IX (CAIX) targeting the tumor cells to study the effect of sunitinib on the biodistribution of Girentuximab because combination of modalities targeting tumor vasculature and tumor cells might result in improved effect. Nude mice with human RCC xenografts (NU12, SK-RC-52) were treated orally with 0.8 mg/day sunitinib, or vehicle for 7 to 14 days. Three days before start or cessation of treatment mice were injected i.v. with 0.4 MBq/5 μg 111In-Girentuximab followed by biodistribution studies. Immunohistochemical analyses were performed to study the tumor vasculature and CAIX expression and to confirm Girentuximab uptake. NU12 appeared to represent a sunitinib sensitive tumor: sunitinib treatment resulted in extensive necrosis and decreased microvessel density (MVD). Accumulation of Girentuximab was significantly decreased when sunitinib treatment preceded the antibody injection but remained unchanged when sunitinib followed Girentuximab injection. Cessation of therapy led to a rapid neovascularization, reminiscent of a tumor flare. SK-RC-52 appeared to represent a sunitinib-resistant tumor: (central) tumor necrosis was minimal and MVD was not affected. Sunitinib treatment resulted in increased Girentuximab uptake, regardless of the sequence of treatment. These data indicate that sunitinib can be combined with Girentuximab. Since these two modalities have different modes of action, this combination might lead to enhanced therapeutic efficacy

Automatic airway segmentation in chest CT using convolutional neural networks

Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations