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

One Step Nucleic Acid Amplification (OSNA) - a new method for lymph node staging in colorectal carcinomas

<p>Abstract</p> <p>Background</p> <p>Accurate histopathological evaluation of resected lymph nodes (LN) is essential for the reliable staging of colorectal carcinomas (CRC). With conventional sectioning and staining techniques usually only parts of the LN are examined which might lead to incorrect tumor staging. A molecular method called OSNA (One Step Nucleic Acid Amplification) may be suitable to determine the metastatic status of the complete LN and therefore improve staging.</p> <p>Methods</p> <p>OSNA is based on a short homogenisation step and subsequent automated amplification of cytokeratin 19 (CK19) mRNA directly from the sample lysate, with result available in 30-40 minutes. In this study 184 frozen LN from 184 patients with CRC were investigated by both OSNA and histology (Haematoxylin & Eosin staining and CK19 immunohistochemistry), with half of the LN used for each method. Samples with discordant results were further analysed by RT-PCR for CK19 and carcinoembryonic antigen (CEA).</p> <p>Results</p> <p>The concordance rate between histology and OSNA was 95.7%. Three LN were histology+/OSNA- and 5 LN histology-/OSNA+. RT-PCR supported the OSNA result in 3 discordant cases, suggesting that metastases were exclusively located in either the tissue analysed by OSNA or the tissue used for histology. If these samples were excluded the concordance was 97.2%, the sensitivity 94.9%, and the specificity 97.9%. Three patients (3%) staged as UICC I or II by routine histopathology were upstaged as LN positive by OSNA. One of these patients developed distant metastases (DMS) during follow up.</p> <p>Conclusion</p> <p>OSNA is a new and reliable method for molecular staging of lymphatic metastases in CRC and enables the examination of whole LN. It can be applied as a rapid diagnostic tool to estimate tumour involvement in LN during the staging of CRC.</p

Method for coregistration of optical measurements of breast tissue with histopathology : the importance of accounting for tissue deformations

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License

Color and morphological features extraction and nuclei classification in tissue samples of colorectal cancer

Cancer is an important public health problem and the third most leading cause of death in North America. Among the highest impact types of cancer are colorectal, breast, lung, and prostate. This thesis addresses the features extraction by using different artificial intelligence algorithms that provide distinct solutions for the purpose of Computer-AidedDiagnosis (CAD). For example, classification algorithms are employed in identifying histological structures, such as lymphocytes, cancer-cells nuclei and glands, from features like existence, extension or shape. The morphological aspect of these structures indicates the degree of severity of the related disease. In this paper, we use a large dataset of 5000 images to classify eight different tissue types in the case of colorectal cancer. We compare results with another dataset. We perform image segmentation and extract statistical information about the area, perimeter, circularity, eccentricity and solidity of the interest points in the image. Finally, we use and compare four popular machine learning techniques, i.e., Naive Bayes, Random Forest, Support Vector Machine and Multilayer Perceptron to classify and to improve the precision of category assignation. The performance of each algorithm was measured using 3 types of metrics: Precision, recall and F1-Score representing a huge contribution to the existing literature complementing it in a quantitative way. The large number of images has helped us to circumvent the overfitting and reproducibility problems. The main contribution is the use of new characteristics different from those already studied, this work researches about the color and morphological characteristics in the images that may be useful for performing tissue classification in colorectal cancer histology

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

© 2018 Awan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The content of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University

Magnetic Resonance Imaging (MRI) Biomarkers for Therapeutic Response Prediction in Rectal Cancer

Prediction of chemoradiotherapy (CRT) response in rectal cancer would enable stratification of management whereby responders could undergo ‘watch-and-wait’ to avoid surgical morbidity, and non-responders could have early treatment intensification to improve therapeutic outcomes. Functional MRI can assess tumour function and heterogeneity, and may improve therapeutic response prediction. The aims of this PhD were to (i) prospectively evaluate multi-parametric MRI at 3.0 tesla in vivo combining diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI for prediction of CRT response and 2 year disease-free survival (DFS), and (ii) examine diffusion tensor imaging (DTI) MRI biomarkers of rectal cancer extent and heterogeneity at ultra-high field 11.7 tesla ex vivo in order to establish a pipeline for MRI biomarker discovery from ultra-high field to clinical field. Patients with locally advanced rectal cancer undergoing CRT followed by surgery underwent multi-parametric MRI before, during, and after CRT. A whole tumour voxelwise histogram analysis of apparent diffusion co-efficient (ADC) and Ktrans heterogeneity was performed and correlated with histopathology tumour regression grade. After CRT (before surgery) ADC 75th and 90th quantiles were significantly higher in responders than non-responders. Patients with higher Ktrans values after CRT or greater increase in Ktrans values from before to after CRT had a significantly higher risk of distant metastases, and lower 2 year DFS. Biobank tissue from patients with rectal cancer were examined at 11.7 tesla and DTI-MRI results correlated with histopathology. This work established a discovery framework for screening Biobank cancer tissue for novel MRI biomarkers of tumour extent and heterogeneity, and resulted in good preservation of tissue integrity and MRI-histopathology alignment. DTI-MRI derived fractional anisotropy (FA) was able to differentiate between tumour and desmoplasia, fibrous tissue, and muscularis propria, allowing for more accurate delineation of rectal cancer tumour extent and stromal heterogeneity ex vivo. In conclusion, DWI-MRI was predictive of CRT response, DCE-MRI was predictive of 2 year DFS, and DTI-MRI was able to more accurately define tumour extent and heterogeneity in rectal cancer. These findings could be useful for stratification of patients for individualised treatment based on accurate assessment of tumour extent and therapeutic response prediction

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue