Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection

Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina. Detecting diabetic retinopathy requires physicians to look at the whole image and fixate on some specific regions to locate potential biomarkers of the disease. Therefore, getting inspiration from ophthalmologist, we propose to combine coarse-grained classifiers that detect discriminating features from the whole images, with a recent breed of fine-grained classifiers that discover and pay particular attention to pathologically significant regions. To evaluate the performance of this proposed ensemble, we used publicly available EyePACS and Messidor datasets. Extensive experimentation for binary, ternary and quaternary classification shows that this ensemble largely outperforms individual image classifiers as well as most of the published works in most training setups for diabetic retinopathy detection. Furthermore, the performance of fine-grained classifiers is found notably superior than coarse-grained image classifiers encouraging the development of task-oriented fine-grained classifiers modelled after specialist ophthalmologists.Comment: Pages 12, Figures

CaDIS: Cataract dataset for surgical RGB-image segmentation

Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/

Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.Comment: Accepted to International Conference on Image Analysis and Recognition, ICIAR 2019. Published at https://doi.org/10.1007/978-3-030-27272-2_29 Code: https://github.com/zoujx96/DR-segmentatio

Fusion Techniques in Biomedical Information Retrieval

For difficult cases clinicians usually use their experience and also the information found in textbooks to determine a diagnosis. Computer tools can help them supply the relevant information now that much medical knowledge is available in digital form. A biomedical search system such as developed in the Khresmoi project (that this chapter partially reuses) has the goal to fulfil information needs of physicians. This chapter concentrates on information needs for medical cases that contain a large variety of data, from free text, structured data to images. Fusion techniques will be compared to combine the various information sources to supply cases similar to an example case given. This can supply physicians with answers to problems similar to the one they are analyzing and can help in diagnosis and treatment planning

FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

Spectral-domain optical coherence tomography (SD-OCT) provides volumetric images of retinal structures with unprecedented detail. Accurate segmentation algorithms and feature quantification in these images, however, are needed to realize the full potential of SD-OCT. The fully automated segmentation algorithm, FloatingCanvas, serves this purpose and performs a volumetric segmentation of retinal tissue layers in three-dimensional image volume acquired around the optic nerve head without requiring any pre-processing. The reconstructed layers are analysed to extract features such as blood vessels and retinal nerve fibre layer thickness. Findings from images obtained with the RTVue-100 SD-OCT (Optovue, Fremont, CA, USA) indicate that FloatingCanvas is computationally efficient and is robust to the noise and low contrast in the images. The FloatingCanvas segmentation demonstrated good agreement with the human manual grading. The retinal nerve fibre layer thickness maps obtained with this method are clinically realistic and highly reproducible compared with time-domain StratusOCT™

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Alteration in P-glycoprotein Functionality Affects Intrabrain Distribution of Quinidine More Than Brain Entry—A Study in Rats Subjected to Status Epilepticus by Kainate

This study aimed to investigate the use of quinidine microdialysis to study potential changes in brain P-glycoprotein functionality after induction of status epilepticus (SE) by kainate. Rats were infused with 10 or 20 mg/kg quinidine over 30 min or 4 h. Plasma, brain extracellular fluid (brain ECF), and end-of-experiment total brain concentrations of quinidine were determined during 7 h after the start of the infusion. Effect of pretreatment with tariquidar (15 mg/kg, administered 30 min before the start of the quinidine infusion) on the brain distribution of quinidine was assessed. This approach was repeated in kainate-treated rats. Quinidine kinetics were analyzed with population modeling (NONMEM). The quinidine microdialysis assay clearly revealed differences in brain distribution upon changes in P-glycoprotein functionality by pre-administration of tariquidar, which resulted in a 7.2-fold increase in brain ECF and a 40-fold increase in total brain quinidine concentration. After kainate treatment alone, however, no difference in quinidine transport across the blood–brain barrier was found, but kainate-treated rats tended to have a lower total brain concentration but a higher brain ECF concentration of quinidine than saline-treated rats. This study did not provide evidence for the hypothesis that P-glycoprotein function at the blood–brain barrier is altered at 1 week after SE induction, but rather suggests that P-glycoprotein function might be altered at the brain parenchymal level

A 1-Year Prospective French Nationwide Study of Emergency Hospital Admissions in Children and Adults with Primary Immunodeficiency.

PURPOSE: Patients with primary immunodeficiency (PID) are at risk of serious complications. However, data on the incidence and causes of emergency hospital admissions are scarce. The primary objective of the present study was to describe emergency hospital admissions among patients with PID, with a view to identifying "at-risk" patient profiles. METHODS: We performed a prospective observational 12-month multicenter study in France via the CEREDIH network of regional PID reference centers from November 2010 to October 2011. All patients with PIDs requiring emergency hospital admission were included. RESULTS: A total of 200 admissions concerned 137 patients (73 adults and 64 children, 53% of whom had antibody deficiencies). Thirty admissions were reported for 16 hematopoietic stem cell transplantation recipients. When considering the 170 admissions of non-transplant patients, 149 (85%) were related to acute infections (respiratory tract infections and gastrointestinal tract infections in 72 (36%) and 34 (17%) of cases, respectively). Seventy-seven percent of the admissions occurred during winter or spring (December to May). The in-hospital mortality rate was 8.8% (12 patients); death was related to a severe infection in 11 cases (8%) and Epstein-Barr virus-induced lymphoma in 1 case. Patients with a central venous catheter (n = 19, 13.9%) were significantly more hospitalized for an infection (94.7%) than for a non-infectious reason (5.3%) (p = 0.04). CONCLUSION: Our data showed that the annual incidence of emergency hospital admission among patients with PID is 3.4%. The leading cause of emergency hospital admission was an acute infection, and having a central venous catheter was associated with a significantly greater risk of admission for an infectious episode