GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques

A model based approach for the characterisation of radiolabelled antibodies in radioimmunotherapy

Radioimmunotherapy (RIT) utilises antibodies directed against tumour associated antigens to carry a therapeutic dose of radiation to the tumour. Using RIT, model tumours have been successfully treated and yet clinical responses have been limited by poor tumour localisation. In an attempt to overcome this, many new antibodies have been developed. Measuring the gross tumour localisation and tumour to normal tissue ratio in animal models has generally been used to assess the potential clinical use of these antibodies. However, these measurements assume all the energy from the electron emitted from the radionuclide is deposited in the source organ, and also ignore the effects of dose-rate and cell proliferation during treatment. In addition, they do not consider the effects of heterogeneous dose deposition and response within the tissues. The principal purpose of this thesis is to develop a more accurate measure of the biological effect of radiolabelled antibodies in a mouse xenograft in order to select the optimal radionuclide/antibody combination for more effective therapy in man. A structural model has been developed from mouse data to facilitate more accurate absorbed dose calculations by accounting for organ size, shape, and position relative to surrounding organs. In addition, the linear-quadratic model, conventionally used in external beam radiotherapy, has been adapted for use in RIT to account for the effects of dose-rate and proliferation during treatment. To characterise heterogeneity of dose deposition and response in tumours, images of tumour morphology and radiolabelled antibody distribution were registered. The images were obtained through digitisation of stained histological sections and storage phosphor plate technology. All data was collected using a wide range of antibodies labelled with 131I and 90Y. These models show that multivalent, tumour-specific antibodies, with intermediate clearance rates, deliver the most effective dose to xenografts. Antibody affinity and avidity facilitate the prolonged retention in radiosensitive areas of tumour where most of the dose is deposited. In addition, a significantly greater activity of 131I can be injected before causing the equivalent bone marrow toxicity. Furthermore, when antibodies are labelled with 90Y, a significant amount of the electron energy escapes the source organ and is absorbed in surrounding tissue. Nevertheless, the results clearly show that radionuclide and antibody should be matched in order to deliver optimum therapy

Estimation of Immune Cell Densities in Immune Cell Conglomerates: An Approach for High-Throughput Quantification

Determining the correct number of positive immune cells in immunohistological sections of colorectal cancer and other tumor entities is emerging as an important clinical predictor and therapy selector for an individual patient. This task is usually obstructed by cell conglomerates of various sizes. We here show that at least in colorectal cancer the inclusion of immune cell conglomerates is indispensable for estimating reliable patient cell counts. Integrating virtual microscopy and image processing principally allows the high-throughput evaluation of complete tissue slides.For such large-scale systems we demonstrate a robust quantitative image processing algorithm for the reproducible quantification of cell conglomerates on CD3 positive T cells in colorectal cancer. While isolated cells (28 to 80 microm(2)) are counted directly, the number of cells contained in a conglomerate is estimated by dividing the area of the conglomerate in thin tissues sections (< or =6 microm) by the median area covered by an isolated T cell which we determined as 58 microm(2). We applied our algorithm to large numbers of CD3 positive T cell conglomerates and compared the results to cell counts obtained manually by two independent observers. While especially for high cell counts, the manual counting showed a deviation of up to 400 cells/mm(2) (41% variation), algorithm-determined T cell numbers generally lay in between the manually observed cell numbers but with perfect reproducibility.In summary, we recommend our approach as an objective and robust strategy for quantifying immune cell densities in immunohistological sections which can be directly implemented into automated full slide image processing systems

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images

Blood vessels in solid tumors are not randomly distributed, but are clustered in angiogenic hotspots. Tumor microvessel density (MVD) within these hotspots correlates with patient survival and is widely used both in diagnostic routine and in clinical trials. Still, these hotspots are usually subjectively defined. There is no unbiased, continuous and explicit representation of tumor vessel distribution in histological whole slide images. This shortcoming distorts angiogenesis measurements and may account for ambiguous results in the literature.In the present study, we describe and evaluate a new method that eliminates this bias and makes angiogenesis quantification more objective and more efficient. Our approach involves automatic slide scanning, automatic image analysis and spatial statistical analysis. By comparing a continuous MVD function of the actual sample to random point patterns, we introduce an objective criterion for hotspot detection: An angiogenic hotspot is defined as a clustering of blood vessels that is very unlikely to occur randomly. We evaluate the proposed method in N=11 images of human colorectal carcinoma samples and compare the results to a blinded human observer. For the first time, we demonstrate the existence of statistically significant hotspots in tumor images and provide a tool to accurately detect these hotspots

γ-H2AX foci as in vivo effect biomarker in children emphasize the importance to minimize x-ray doses in paediatric CT imaging

Objectives: Investigation of DNA damage induced by CT x-rays in paediatric patients versus patient dose in a multicentre setting. Methods: From 51 paediatric patients (median age, 3.8 years) who underwent an abdomen or chest CT examination in one of the five participating radiology departments, blood samples were taken before and shortly after the examination. DNA damage was estimated by scoring gamma-H2AX foci in peripheral blood T lymphocytes. Patient-specific organ and tissue doses were calculated with a validated Monte Carlo program. Individual lifetime attributable risks (LAR) for cancer incidence and mortality were estimated according to the BEIR VII risk models. Results: Despite the low CT doses, a median increase of 0.13 gamma-H2AX foci/cell was observed. Plotting the induced gamma-H2AX foci versus blood dose indicated a low-dose hypersensitivity, supported also by an in vitro dose-response study. Differences in dose levels between radiology centres were reflected in differences in DNA damage. LAR of cancer mortality for the paediatric chest CT and abdomen CT cohort was 0.08 and 0.13% respectively. Conclusion: CT x-rays induce DNA damage in paediatric patients even at low doses and the level of DNA damage is reduced by application of more effective CT dose reduction techniques and paediatric protocols

A non-invasive approach to monitor chronic lymphocytic leukemia engraftment in a xenograft mouse model using ultra-small superparamagnetic iron oxide-magnetic resonance imaging (USPIO-MRI).

This work was supported by: Associazione Italiana Ricerca sul Cancro (AIRC) [Grant 5 x mille n.9980, (to M.F., F.M. and A. N.)]; AIRC I.G. [n. 14,326 (to M.F.)], [n.10136 and 16,722 (A.N.)], [n.15426 (to F.F.)]. AIRC and Fondazione CaRiCal co-financed Multi Unit Regional Grant 2014 [n.16695 (to F.M.)]. Italian Ministry of Health 5 × 1000 funds (to F.F). A.G R. was supported by Associazione Italiana contro le Leucemie-Linfomi-Mielomi (AIL) Cosenza - Fondazione Amelia Scorza (FAS). S.M. C.M., F.V., L. E., S. B., were supported by AIRC.Peer reviewedPostprin