132 research outputs found
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
Deep learning is currently the state-of-the-art for automated detection of
referable diabetic retinopathy (DR) from color fundus photographs (CFP). While
the general interest is put on improving results through methodological
innovations, it is not clear how good these approaches perform compared to
standard deep classification models trained with the appropriate settings. In
this paper we propose to model a strong baseline for this task based on a
simple and standard ResNet-18 architecture. To this end, we built on top of
prior art by training the model with a standard preprocessing strategy but
using images from several public sources and an empirically calibrated data
augmentation setting. To evaluate its performance, we covered multiple
clinically relevant perspectives, including image and patient level DR
screening, discriminating responses by input quality and DR grade, assessing
model uncertainties and analyzing its results in a qualitative manner. With no
other methodological innovation than a carefully designed training, our ResNet
model achieved an AUC = 0.955 (0.953 - 0.956) on a combined test set of 61007
test images from different public datasets, which is in line or even better
than what other more complex deep learning models reported in the literature.
Similar AUC values were obtained in 480 images from two separate in-house
databases specially prepared for this study, which emphasize its generalization
ability. This confirms that standard networks can still be strong baselines for
this task if properly trained.Comment: Accepted for publication at the 18th International Symposium on
Medical Information Processing and Analysis (SIPAIM 2022
Learning normal asymmetry representations for homologous brain structures
Although normal homologous brain structures are approximately symmetrical by definition, they also have shape differences due
to e.g. natural ageing. On the other hand, neurodegenerative conditions
induce their own changes in this asymmetry, making them more pronounced or altering their location. Identifying when these alterations
are due to a pathological deterioration is still challenging. Current clinical tools rely either on subjective evaluations, basic volume measurements or disease-specific deep learning models. This paper introduces a
novel method to learn normal asymmetry patterns in homologous brain
structures based on anomaly detection and representation learning. Our
framework uses a Siamese architecture to map 3D segmentations of left
and right hemispherical sides of a brain structure to a normal asymmetry embedding space, learned using a support vector data description objective. Being trained using healthy samples only, it can quantify
deviations-from-normal-asymmetry patterns in unseen samples by measuring the distance of their embeddings to the center of the learned normal space. We demonstrate in public and in-house sets that our method
can accurately characterize normal asymmetries and detect pathological
alterations due to Alzheimer’s disease and hippocampal sclerosis, even
though no diseased cases were accessed for training. Our source code is
available at https://github.com/duiliod/DeepNORH
Learning normal asymmetry representations for homologous brain structures
Although normal homologous brain structures are approximately symmetrical by
definition, they also have shape differences due to e.g. natural ageing. On the
other hand, neurodegenerative conditions induce their own changes in this
asymmetry, making them more pronounced or altering their location. Identifying
when these alterations are due to a pathological deterioration is still
challenging. Current clinical tools rely either on subjective evaluations,
basic volume measurements or disease-specific deep learning models. This paper
introduces a novel method to learn normal asymmetry patterns in homologous
brain structures based on anomaly detection and representation learning. Our
framework uses a Siamese architecture to map 3D segmentations of left and right
hemispherical sides of a brain structure to a normal asymmetry embedding space,
learned using a support vector data description objective. Being trained using
healthy samples only, it can quantify deviations-from-normal-asymmetry patterns
in unseen samples by measuring the distance of their embeddings to the center
of the learned normal space. We demonstrate in public and in-house sets that
our method can accurately characterize normal asymmetries and detect
pathological alterations due to Alzheimer's disease and hippocampal sclerosis,
even though no diseased cases were accessed for training. Our source code is
available at https://github.com/duiliod/DeepNORHA
SketchZooms: Deep Multi-view Descriptors for Matching Line Drawings
Finding point-wise correspondences between images is a long-standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper, we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non-photorealistic rendering over a large collection of part-based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown togeneralize correctly in unseen sketches hand-drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.Fil: Navarro, Jose Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ingeniería - Sede Puerto Madryn. Departamento de Informática; ArgentinaFil: Orlando, José Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Iarussi, Emmanuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentin
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Optical coherence tomography (OCT) has become the most important imaging
modality in ophthalmology. A substantial amount of research has recently been
devoted to the development of machine learning (ML) models for the
identification and quantification of pathological features in OCT images. Among
the several sources of variability the ML models have to deal with, a major
factor is the acquisition device, which can limit the ML model's
generalizability. In this paper, we propose to reduce the image variability
across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an
unsupervised unpaired image transformation algorithm. The usefulness of this
approach is evaluated in the setting of retinal fluid segmentation, namely
intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a
segmentation model on images acquired with a source OCT device. Then we
evaluate the model on (1) source, (2) target and (3) transformed versions of
the target OCT images. The presented transformation strategy shows an F1 score
of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional
transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin)
--------------- Accepted for publication in the "IEEE International Symposium
on Biomedical Imaging (ISBI) 2019
An ensemble deep learning based approach for red lesion detection in fundus images
Background and objectives: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. Methods: In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. Results: We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Conclusions: Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our system is publicly available at https://github.com/ignaciorlando/red-lesion-detection.Fil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Prokofyeva, Elena. Scientific Institute of Public Health; BélgicaFil: del Fresno, Mirta Mariana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Blaschko, Matthew Brian. ESAT Speech Group; Bélgic
NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.La versión final de este artículo fue publicada el 29 de junio de 2023 en Brain Topography (Springer). Se encuentra accesible desde Biblioteca Di Tella a través de Prim
Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures
In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.Fil: Hofer, Dominik. Medizinische Universität Wien; AustriaFil: Schmidt Erfurth, Ursula. Medizinische Universität Wien; AustriaFil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Medizinische Universität Wien; AustriaFil: Goldbach, Felix. Medizinische Universität Wien; AustriaFil: Gerendas, Bianca S.. Medizinische Universität Wien; AustriaFil: Seeböck, Philipp. Medizinische Universität Wien; Austri
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