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

Interstitial lung disease in Malta

Aim: To establish the prevalence, management and response to treatment of interstitial lung disease (ILD) in Malta. Methodology: The personal files of 102 living and 26 deceased patients with ILD under the care of 4 respiratory physicians were reviewed retrospectively. The investigations utilised for reaching the diagnosis, patient management and response to treatment were analysed. Results: The prevalence of ILD was estimated at 24.9 per 100,000 population. Pulmonary function tests were performed at least once in 109 patients (n=128, 85%), and pletysmography and exercise oximetry in 36 patients (n=128, 28%). A chest x-ray (CXR) was performed in 120 patients (n=128, 93.7%), of which 8 (n=120, 6.66%) were normal, a computed tomography scan of the thorax in 113 patients (n=128, 88.3%), all of which showed fibrotic changes and a DTPA scan in 17 patients (n=128, 13.3%). Regarding more invasive investigations, bronchoalveolar lavage was performed in 10 patients (n=128, 7.8%), open lung biopsy in 4 patients (n=128, 3.1 %), video-assisted thoracoscopic surgery in 4 patients (n=128, 3.1%) and transbronchial lung biopsy in 7 patients (n=128, 5.5%). Corticosteroids were the most common drugs prescribed in 64 patients (n=128, 50%) followed by azathioprine in 23 patients (n=128, 18%) and cyclophosphamide in 3 patients (n=128, 2.3%). There was a definite worsening in lung function associated with increasing age. There was no standardisation of follow up for these patients. Conclusion: The method of diagnosis, management and follow up of patients with ILD locally requires improvement to optimise standards of care and hence compare with proposed international guidelines.peer-reviewe

Extracting Lungs from CT Images using Fully Convolutional Networks

Analysis of cancer and other pathological diseases, like the interstitial lung diseases (ILDs), is usually possible through Computed Tomography (CT) scans. To aid this, a preprocessing step of segmentation is performed to reduce the area to be analyzed, segmenting the lungs and removing unimportant regions. Generally, complex methods are developed to extract the lung region, also using hand-made feature extractors to enhance segmentation. With the popularity of deep learning techniques and its automated feature learning, we propose a lung segmentation approach using fully convolutional networks (FCNs) combined with fully connected conditional random fields (CRF), employed in many state-of-the-art segmentation works. Aiming to develop a generalized approach, the publicly available datasets from University Hospitals of Geneva (HUG) and VESSEL12 challenge were studied, including many healthy and pathological CT scans for evaluation. Experiments using the dataset individually, its trained model on the other dataset and a combination of both datasets were employed. Dice scores of $98.67\%\pm0.94\%$ for the HUG-ILD dataset and $99.19\%\pm0.37\%$ for the VESSEL12 dataset were achieved, outperforming works in the former and obtaining similar state-of-the-art results in the latter dataset, showing the capability in using deep learning approaches.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Purpose: Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Methods: Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. Results: In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. Conclusions: The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the syste