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

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Radiomics analysis in gastrointestinal imaging: a narrative review

Background and Objective: To present an overview of radiomics radiological applications in major gastrointestinal oncological non-oncologic diseases, such as colorectal cancer, pancreatic cancer, gastro- oesophageal cancer, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and non-oncologic diseases, such as liver fibrosis, nonalcoholic steatohepatitis, and inflammatory bowel disease. Methods: A search of PubMed databases was performed for the terms “radiomic”, “radiomics”, “liver”, “small bowel”, “colon”, “GI tract”, and “gastrointestinal imaging” for English articles published between January 2013 and July 2022. A narrative review was undertaken to summarize literature pertaining to application of radiomics in major oncological and non-oncological gastrointestinal diseases. The strengths and limitation of radiomics, as well as advantages and major limitations and providing considerations for future development of radiomics were discussed. Key Content and Findings: Radiomics consists in extracting and analyzing a vast amount of quantitative features from medical datasets, Radiomics refers to the extraction and analysis of large amounts of quantitative features from medical images. The extraction of these data, integrated with clinical data, allows the construction of descriptive and predictive models that can build disease-specific radiomic signatures. Texture analysis has emerged as one of the most important biomarkers able to assess tumor heterogeneity and can provide microscopic image information that cannot be identified with the naked eye by radiologists. Conclusions: Radiomics and texture analysis are currently under active investigation in several institutions worldwide, this approach is being tested in a multitude of anatomical areas and diseases, with the final aim to exploit personalized medicine in diagnosis, treatment planning, and prediction of outcomes. Despite promising initial results, the implementation of radiomics is still hampered by some limitations related to the lack of standardization and validation of image acquisition protocols, feature segmentation, data extraction, processing, and analysi

Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks

Purpose: This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods: Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results: The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion: This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance.</p

Radiogenomics in non-small-cell lung cancer

Ο μη μικροκυτταρικός καρκίνος του πνεύμονα είναι ο πιο συχνά συναντώμενος υποτύπος καρκίνου του πνεύμονα, ο οποίος αποτελείται από ένα φάσμα υποτύπων. Το NSCLC είναι ένας θανατηφόρος, ετερογενής συμπαγής όγκος με μια εκτεταμένη σειρά μοριακών χαρακτηριστικών. Η πάθηση έχει γίνει ένα αξιοσημείωτο παράδειγμα ιατρικής ακριβείας καθώς το ενδιαφέρον για το θέμα συνεχίζει να επεκτείνεται. Ο απώτερος στόχος της τρέχουσας έρευνας είναι να χρησιμοποιήσει συγκεκριμένα γονίδια ως βιοδείκτες για την πρόγνωση, την έγκαιρη διάγνωση και την εξατομικευμένη θεραπεία, τα οποία διευκολύνονται από τη χρήση εξελισσόμενων τεχνικών αλληλούχισης επόμενης γενιάς που επιτρέπουν την ταυτόχρονη ανίχνευση μεγάλου αριθμού γενετικές ανωμαλίες. Γνωστές μεταλλάξεις ενός αριθμού γονιδίων, όπως τα EGFR, ALK και KRAS, επηρεάζουν ήδη τις αποφάσεις θεραπείας και νέα βασικά γονίδια και μοριακές υπογραφές διερευνώνται για την προγνωστική τους αξία καθώς και για την πιθανή συμβολή τους στην ανοσοθεραπεία και τη θεραπεία της υποτροπής στην αντίσταση στις υπάρχουσες θεραπείες. Οι τύποι δειγμάτων που χρησιμοποιούνται για μελέτες NGS, όπως αναρροφήσεις με λεπτή βελόνα, ιστός ενσωματωμένος σε παραφίνη σταθεροποιημένος με φορμαλίνη και DNA χωρίς κύτταρα, έχουν ο καθένας τα δικά του πλεονεκτήματα και μειονεκτήματα που πρέπει να ληφθούν υπόψηNon-small cell lung cancer is the most often encountered subtype of lung cancer, which consists of a spectrum of subtypes. NSCLC is a lethal, heterogeneous solid tumor with an extensive array of molecular features. The condition has become a notable example of precision medicine as interest in the topic continues to expand. The ultimate goal of the current research is to use specific genes as biomarkers for its prognosis, timely diagnosis, and personalized therapy, all of which are facilitated by the use of evolving next-generation sequencing techniques that permit the simultaneous detection of a large number of genetic abnormalities. Known mutations of a number of genes, such as EGFR, ALK, and KRAS, already influence treatment decisions, and new key genes and molecular signatures are being investigated for their prognostic value as well as their potential contribution to immunotherapy and the treatment of recurrence due to resistance to existing therapies. The sample types utilized for NGS studies, such as fine-needle aspirates, formalin-fixed paraffin-embedded tissue, and cell-free DNA, each have their own advantages and disadvantages that must be taken into accoun