Estudio y diseño de modelos de aprendizaje profundo para generación multimodal de imagen oftalmológica

[Resumen] En el ámbito oftalmológico, para realizar algunos procesos de diagnóstico, se suele disponer de diferentes modalidades de imagen del fondo de ojo. Generalmente, estas son de dos tipos: invasivas —implican la agresión del organismo—, y no-invasivas —no implican tal agresión y suelen ser más baratas y sencillas de obtener—. En este trabajo, se estudian y adaptan diferentes modelos de aprendizaje profundo (redes de neuronas convolucionales) para la generación de una modalidad de imagen invasiva, la angiografía, a partir de otra no invasiva, la retinografía, en lo que se conoce como paradigma de reconstrucción multimodal. Además, el entrenamiento de estos modelos ha permitido estudiar su utilidad como preentrenamientos de una segunda tarea de carácter finalista, la segmentación de vasos en retinografía, mediante la aplicación de la técnica de transfer learning. La comparación de los resultados de estos modelos para las dos tareas con los de uno de referencia (con un diseño más clásico y sencillo) ha permitido comprobar dos cosas. En primer lugar, que los resultados de estos modelos, aunque se acercan, no alcanzan en muchos casos a los del modelo de referencia. Y en segundo lugar, que la utilización de modelos preentrenados, independientemente de la arquitectura empleada, tiene un impacto positivo en los resultados. Con ellos, las segmentaciones son mejores con un menor tiempo de entrenamiento. La primera de estas observaciones nos han permitido, primero, cuestionar la relevancia de las variantes arquitecturales y principios de diseño de los modelos nuevos en el dominio escogido; y segundo, confirmar la adecuación de dichos modelos generativos a la tarea de reconstrucción multimodal, permitiendo validar el propio paradigma. Por otra parte, la segunda de las observaciones nos ha permitido confirmar la utilidad de los preentrenamientos de la tarea de reconstrucción. Cuando se dispone de pocos datos etiquetados, este tipo de preentrenamientos surgen como una buena opción para mitigar esa escasez y mejorar así los resultados.[Abstract] In the ophthalmological field, different modalities of fundus imaging are usually available to perform some diagnosis. These modalities are of two types: invasive —they involve the aggression of the organism—, and non-invasive —they do not imply such aggression and are usually cheaper and easier to obtain—. In this work are studied and adapted different deep learning models (convolutional neural networks) to generate an invasive imaging modality —angiography— from another non-invasive modality —retinography—. This is known as multimodal reconstruction paradigm. In addition, the training of these models has allowed us to study their usefulness as pre-trainined models of second finalist task: vessel segmentation in retinography. This is achieved through the application of transfer learning technique. The results obtained by the selected models in both tasks were compared to those of a reference model with a more classic and simple design. This comparison showed two points. First, that the results of the selected models did not reach in many cases those of the reference model. Second, that the use of pre-trained models, regardless of the architecture used, has a positive impact on the results. When transfer learning is applied, segmentation images are better with less training time. The first point allowed us to question the relevance of the architectural variants and design principles of the selected models in the chosen domain. Moreover, it confirmed the adequacy of generative models to the task of multimodal reconstruction, validating the paradigm itself. On the other hand, the second point allowed us to confirm the usefulness of pre-trainings from the reconstruction task. This type of pre-training is able to mitigate data scarcity in finalist tasks, and therefore improve the results

Primera cita de Neoscona byzanthina (Pavesi, 1876) (Araneae, Araneidae) en la Península Ibérica

Neoscona byzanthina (Pavesi, 1786) (Araneae, Araneidae) is reported for the first time in the Iberian Peninsula. The specimen was captured on October 2018 in a holm-oak dehesa (Extremadura, Spain).Se hace referencia a la primera observación de Neoscona byzanthina (Pavesi, 1876) (Araneae, Araneidae) de la Península Ibérica. El ejemplar fue capturado en octubre de 2018 en un encinar adehesado (Extremadura, España)

SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT

The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation due to its impressive capabilities and prompt-based interface. While SAM has already been extensively evaluated in various domains, its adaptation to retinal OCT scans remains unexplored. To bridge this research gap, we conduct a comprehensive evaluation of SAM and its adaptations on a large-scale public dataset of OCTs from RETOUCH challenge. Our evaluation covers diverse retinal diseases, fluid compartments, and device vendors, comparing SAM against state-of-the-art retinal fluid segmentation methods. Through our analysis, we showcase adapted SAM's efficacy as a powerful segmentation model in retinal OCT scans, although still lagging behind established methods in some circumstances. The findings highlight SAM's adaptability and robustness, showcasing its utility as a valuable tool in retinal OCT image analysis and paving the way for further advancements in this domain

Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus Images

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases. An exhaustive analysis involves segmenting the retinal vessels and classifying them into veins and arteries. In this work, we present an accurate approach, based on deep neural networks, for the joint segmentation and classification of the retinal veins and arteries from color fundus images. The presented approach decomposes this joint task into three related subtasks: the segmentation of arteries, veins and the whole vascular tree. The experiments performed show that our method achieves competitive results in the discrimination of arteries and veins, while clearly enhancing the segmentation of the different structures. Moreover, unlike other approaches, our method allows for the straightforward detection of vessel crossings, and preserves the continuity of the arterial and venous vascular trees at these locations.This work was funded by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project; Ministerio de Ciencia e Innovación, Government of Spain, through the RTI2018-095894-B-I00 and PID2019-108435RB-I00 research projects; Axencia Galega de Innovación (GAIN), Xunta de Galicia, ref. IN845D 2020/38; Xunta de Galicia and European Social Fund (ESF) of the EU through the predoctoral grant contracts ED481A-2017/328 and ED481A 2021/140; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, is funded by Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%)Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED481A-2017/328Xunta de Galicia; ED481A 2021/140Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0

Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning

[Abstract]: Background and Objectives: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some promising treatments have been proposed that effectively slow down its development, their effectiveness significantly diminishes in the advanced stages. This emphasizes the importance of large-scale screening programs for early detection. Nevertheless, implementing such programs for a disease like AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. For the characterization of the disease, clinicians have to identify and localize certain retinal lesions. All this motivates the development of automatic diagnostic methods. In this sense, several works have achieved highly positive results for AMD detection using convolutional neural networks (CNNs). However, none of them incorporates explainability mechanisms linking the diagnosis to its related lesions to help clinicians to better understand the decisions of the models. This is specially relevant, since the absence of such mechanisms limits the application of automatic methods in the clinical practice. In that regard, we propose an explainable deep learning approach for the diagnosis of AMD via the joint identification of its associated retinal lesions. Methods: In our proposal, a CNN with a custom architectural setting is trained end-to-end for the joint identification of AMD and its associated retinal lesions. With the proposed setting, the lesion identification is directly derived from independent lesion activation maps; then, the diagnosis is obtained from the identified lesions. The training is performed end-to-end using image-level labels. Thus, lesion-specific activation maps are learned in a weakly-supervised manner. The provided lesion information is of high clinical interest, as it allows clinicians to assess the developmental stage of the disease. Additionally, the proposed approach allows to explain the diagnosis obtained by the models directly from the identified lesions and their corresponding activation maps. The training data necessary for the approach can be obtained without much extra work on the part of clinicians, since the lesion information is habitually present in medical records. This is an important advantage over other methods, including fully-supervised lesion segmentation methods, which require pixel-level labels whose acquisition is arduous. Results: The experiments conducted in 4 different datasets demonstrate that the proposed approach is able to identify AMD and its associated lesions with satisfactory performance. Moreover, the evaluation of the lesion activation maps shows that the models trained using the proposed approach are able to identify the pathological areas within the image and, in most cases, to correctly determine to which lesion they correspond. Conclusions: The proposed approach provides meaningful information—lesion identification and lesion activation maps—that conveniently explains and complements the diagnosis, and is of particular interest to clinicians for the diagnostic process. Moreover, the data needed to train the networks using the proposed approach is commonly easy to obtain, what represents an important advantage in fields with particularly scarce data, such as medical imaging.Xunta de Galicia; ED481B-2022-025Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED481A 2021/140Xunta de Galicia; ED431G 2019/01This work was funded by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project; Ministerio de Ciencia e Innovación, Government of Spain, through RTI2018-095894-B-I00 and PID2019-108435RB-I00 research projects; Axencia Galega de Innovación (GAIN), Xunta de Galicia, ref. IN845D 2020/38; Conselleria de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva, ref. ED431C 2020/24, the predoctoral grant ref. ED481A 2021/140, and the postdoctoral grant ref. ED481B-2022-025; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, is funded by Conselleria de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaria Xeral de Universidades (20%)

Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation

Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D-to-2D. However, the performance of existing methods is strongly conditioned by the amount of labeled data available, as there is currently no data efficient method, e.g. transfer learning, that has been validated on these tasks. In this work, we propose a novel convolutional neural network (CNN) and self-supervised learning (SSL) method for label-efficient 3D-to-2D segmentation. The CNN is composed of a 3D encoder and a 2D decoder connected by novel 3D-to-2D blocks. The SSL method consists of reconstructing image pairs of modalities with different dimensionality. The approach has been validated in two tasks with clinical relevance: the en-face segmentation of geographic atrophy and reticular pseudodrusen in optical coherence tomography. Results on different datasets demonstrate that the proposed CNN significantly improves the state of the art in scenarios with limited labeled data by up to 8% in Dice score. Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.Comment: To appear in MICCAI 2023. Code: https://github.com/j-morano/multimodal-ssl-fp

La colección de Araneidos del Departamento de Zoología de la Universidad de Salamanca, IV : familias Argiopidae, Tetragnathidae, Zodariidae, y Urocteidae

Se ofrecen nuevos datos para 22 especies de arañas pertenecientes a cuatro familias (Argiopidae, Tetragnathidae, Zodariidae, y Urocteidae) con algunos comentarios de interés sobre su distribución o su identidad específica.In this paper, new data for 22 species of families Argiopidae, Tetragnathidae, Zodariidae, and Urocteidae, are enuemrated; and some discussions of their distribution and their taxonomical identity are introduced

Differential Binary Encoding Method for Calibrating Image Sensors Based on IOFBs

Image transmission using incoherent optical fiber bundles (IOFBs) requires prior calibration to obtain the spatial in-out fiber correspondence necessary to reconstruct the image captured by the pseudo-sensor. This information is recorded in a Look-Up Table called the Reconstruction Table (RT), used later for reordering the fiber positions and reconstructing the original image. This paper presents a very fast method based on image-scanning using spaces encoded by a weighted binary code to obtain the in-out correspondence. The results demonstrate that this technique yields a remarkable reduction in processing time and the image reconstruction quality is very good compared to previous techniques based on spot or line scanning, for example

Industry 4.0 Competencies as the Core of Online Engineering Laboratories

Online laboratories are widely used in higher engineering education and due to the COVID-19 pandemic, they have taken on an even greater relevance. At Tecnologico de Monterrey, Mexico, well-established techniques such as Problem-Based Learning (PBL), Project-Oriented Learning (POL) and Research-Based Learning (RBL) have been implemented over the years, and over the past year, have been successfully incorporated into the students’ learning process within online and remote laboratories. Nevertheless, these learning techniques do not include an element which is crucial in today’s industrialized world: Industry 4.0 competencies. Therefore, this work aims to describe a pedagogical approach in which the development of Industry based competencies complements the aforementioned learning techniques. The use and creation of virtual environments and products is merged with the understanding of fundamental engineering concepts. Further, a measurement of the students’ perceived self-efficacy related to this pedagogical approach is carried out, focusing on the physiological states and mastery experiences of the students. An analysis of its results is presented as well as a discussion on these findings, coupled with the perspectives from different key stakeholders on the importance of the educational institutions’ involvement in developing Industry 4.0 competencies in engineering students. Finally, comments regarding additional factors which play a role in the educational process, but were not studied at this time, as well as additional areas of interest are given

Liver-related events and mortality among elderly patients with advanced chronic hepatitis C treated with direct-acting antivirals

Research article[Abstract] BACKGROUND: Direct-acting antivirals (DAAs) are effective in patients aged ≥65 years. However, little is known about the effects of DAAs on survival, liver decompensation and development of hepatocellular carcinoma (HCC). OBJECTIVE: To compare the incidence of liver-related events and mortality between patients aged ≥65 and <65 years. METHODS: Prospective study comparing patients aged ≥65 and <65 years treated with DAAs. The incidence of liver-related events and mortality, and HCC was compared between age groups. RESULTS: Five hundred patients (120 aged ≥65 and 380 aged <65 years) were included. The incidence of liver-related events was 2.62 per 100 patient-years (py) in older and 1.41/100 py in younger patients. All-cause mortality was 3.89 and 1.27/100 py in older and younger patients, respectively. The respective liver-related mortality rates were 1.12 and 0.31/100 py. In patients with cirrhosis (stage F4), all-cause mortality (P = 0.283) and liver-related mortality (P = 0.254) did not differ between groups. All five liver-related deaths were related to multifocal HCC. The incidence of HCC was 1.91 and 1.43 per 100 py in the older and younger groups, respectively (P = 0.747). The diagnosis of HCC was 8 months after the end of treatment. CONCLUSIONS: The incidence of liver-related events and liver-related mortality was low in older people treated with DAAs and was similar to that in younger patients. The extra mortality in people aged ≥65 years treated with DAAs seems to be secondary to non-liver-related causes. These results support the utilization of DAAs in patients aged ≥65 years.Instituto de Salud Carlos III; JR17/0002