Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new avenues of compressing and indexing video in an extremely efficient manner. In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images. To boost the accuracy of video image retrieval and to address data variability like multi-modality and view-point changes, we propose the integration of a Siamese network. We demonstrate that our approach is competitive in retrieving images from 3 large scale videos of 3 different patients obtained against the query samples of their previous diagnosis. Quantitative validation shows that the combined approach yield an overall improvement of 5% and 8% over classical and variational autoencoders, respectively.Comment: Accepted at IEEE International Symposium on Biomedical Imaging (ISBI), 201

Foreign Object Detection and Quantification of Fat Content Using A Novel Multiplexing Electric Field Sensor

There is an ever growing need to ensure the quality of food and assess specific quality parameters in all the links of the food chain, ranging from processing, distribution and retail to preparing food. Various imaging and sensing technologies, including X-ray imaging, ultrasound, and near infrared reflectance spectroscopy have been applied to the problem. Cost and other constraints restrict the application of some of these technologies. In this study we test a novel Multiplexing Electric Field Sensor (MEFS), an approach that allows for a completely non-invasive and non-destructive testing approach. Our experiments demonstrate the reliable detection of certain foreign objects and provide evidence that this sensor technology has the capability of measuring fat content in minced meat. Given the fact that this technology can already be deployed at very low cost, low maintenance and in various different form factors, we conclude that this type of MEFS is an extremely promising technology for addressing specific food quality issues

SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis

Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a large amount of training data and face issues in generalisability to unseen datasets that hinder clinical translation. Endoscopic imaging data incorporates large inter- and intra-patient variability that makes these models more challenging to learn representative features for downstream tasks. Thus, despite the publicly available datasets and datasets that can be generated within hospitals, most supervised models still underperform. While self-supervised learning has addressed this problem to some extent in natural scene data, there is a considerable performance gap in the medical image domain. In this paper, we propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin within the cosine similarity metrics. Our novel approach enables models to learn to cluster similar representative patches, thereby improving their ability to provide better separation between different classes. Our results demonstrate significant improvement on all metrics over the state-of-the-art (SOTA) methods on the test set from the same and diverse datasets. We evaluated our approach for classification, detection, and segmentation. SSL-CPCD achieves 79.77% on Top 1 accuracy for ulcerative colitis classification, 88.62% on mAP for polyp detection, and 82.32% on dice similarity coefficient for segmentation tasks are nearly over 4%, 2%, and 3%, respectively, compared to the baseline architectures. We also demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting nearly 7% improvement in our generalisability assessment.Comment: 1

A deep learning framework for quality assessment and restoration in video endoscopy

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. We propose a fully automatic framework that can: 1) detect and classify six different primary artifacts, 2) provide a quality score for each frame and 3) restore mildly corrupted frames. To detect different artifacts our framework exploits fast multi-scale, single stage convolutional neural network detector. We introduce a quality metric to assess frame quality and predict image restoration success. Generative adversarial networks with carefully chosen regularization are finally used to restore corrupted frames. Our detector yields the highest mean average precision (mAP at 5% threshold) of 49.0 and the lowest computational time of 88 ms allowing for accurate real-time processing. Our restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos we show that our approach preserves an average of 68.7% which is 25% more frames than that retained from the raw videos.Comment: 14 page

Leveraging inter-annotator disagreement for semi-supervised segmentation

The low signal-to-noise ratio typically found in biomedical images often leads experts to disagree about the underlying ground-truth segmentation. While existing approaches for multiple annotations try to resolve conflicting annotations, we instead focus on efficiently using pixels of disagreement to estimate areas of high uncertainty in the data and exploit this information for semi-supervised segmentation.Pseudo-labelling approaches, which utilise unlabelled data by trying to match their own predictions, need to distinguish reliable from unreliable predictions. We propose to identify unreliable pseudo-labels from the output of a separate network that is trained to predict the uncertainty in the data based on conflicting annotations from different annotators.Compared to other uncertainty estimation techniques like MC-Dropout or ensembling approaches, our approach has the two key advantages that its estimates stem directly from the data and that it is computationally more efficient. Using two public datasets, we show the effectiveness of our approach

Active data enrichment by learning what to annotate in digital pathology

Our work aims to link pathology with radiology with the goal to improve the early detection of lung cancer. Rather than utilising a set of predefined radiomics features, we propose to learn a new set of features from histology. Generating a comprehensive lung histology report is the first vital step toward this goal. Deep learning has revolutionised the computational assessment of digital pathology images. Today, we have mature algorithms for assessing morphological features at the cellular and tissue levels. In addition, there are promising efforts that link morphological features with biologically relevant information. While promising, these efforts mostly focus on narrow, well-defined questions. Developing a comprehensive report that is required in our setting requires an annotation strategy that captures all clinically relevant patterns specified in the WHO guidelines. Here, we propose and compare approaches aimed to balance the dataset and mitigate the biases in learning by automatically prioritising regions with clinical patterns underrepresented in the dataset. Our study demonstrates the opportunities active data enrichment can provide and results in a new lung-cancer dataset annotated to a degree that is not readily available in the public domain

Quantitative interpretation of bone marrow biopsies in MPN—what's the point in a molecular age?

The diagnosis of myeloproliferative neoplasms (MPN) requires the integration of clinical, morphological, genetic and immunophenotypic findings. Recently, there has been a transformation in our understanding of the cellular and molecular mechanisms underlying disease initiation and progression in MPN. This has been accompanied by the widespread application of high-resolution quantitative molecular techniques. By contrast, microscopic interpretation of bone marrow biopsies by haematologists/haematopathologists remains subjective and qualitative. However, advances in tissue image analysis and artificial intelligence (AI) promise to transform haematopathology. Pioneering studies in bone marrow image analysis offer to refine our understanding of the boundaries between reactive samples and MPN subtypes and better capture the morphological correlates of high-risk disease. They also demonstrate potential to improve the evaluation of current and novel therapeutics for MPN and other blood cancers. With increased therapeutic targeting of diverse molecular, cellular and extra-cellular components of the marrow, these approaches can address the unmet need for improved objective and quantitative measures of disease modification in the context of clinical trials. This review focuses on the state-of-the-art in image analysis/AI of bone marrow tissue, with an emphasis on its potential to complement and inform future clinical studies and research in MPN

Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring. Presently, endoscopic characterisation is largely operator dependant leading to sometimes undesirable clinical outcomes for patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which is widely used but requires the reliable identification of subtle changes in mucosal inflammation. Most existing deep learning classification methods cannot detect these fine-grained changes which make UC grading such a challenging task. In this work, we introduce a novel patch-level instance-group discrimination with pretext-invariant representation learning (PLD-PIRL) for self-supervised learning (SSL). Our experiments demonstrate both improved accuracy and robustness compared to the baseline supervised network and several state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised classification our proposed PLD-PIRL obtained an improvement of 4.75% on hold-out test data and 6.64% on unseen center test data for top-1 accuracy.Comment: 1