|
Records |
Links |
|
Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
Type |
Conference Article |
|
Year |
2018 |
Publication |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
Abbreviated Journal |
|
|
|
Volume |
11041 |
Issue |
|
Pages |
|
|
|
Keywords |
Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
|
|
Abstract |
Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
|
|
Address |
Granada; September 2018 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
MICCAIW |
|
|
Notes |
IAM; 600.096; 600.075; 601.323; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RSB2018b |
Serial |
3137 |
|
Permanent link to this record |
|
|
|
|
Author |
Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Selection of Radiomics Features based on their Reproducibility |
Type |
Conference Article |
|
Year |
2019 |
Publication |
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
403-408 |
|
|
Keywords |
|
|
|
Abstract |
Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. |
|
|
Address |
Berlin; Alemanya; July 2019 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
EMBC |
|
|
Notes |
IAM; 600.139; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ LTS2019 |
Serial |
3358 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Data Augmentation from Sketch |
Type |
Conference Article |
|
Year |
2019 |
Publication |
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging |
Abbreviated Journal |
|
|
|
Volume |
11840 |
Issue |
|
Pages |
155-162 |
|
|
Keywords |
Data augmentation; cycleGANs; Multi-objective optimization |
|
|
Abstract |
State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.
We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives. |
|
|
Address |
Shenzhen; China; October 2019 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CLIP |
|
|
Notes |
IAM; 600.145; 601.337; 600.139; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GES2019 |
Serial |
3359 |
|
Permanent link to this record |
|
|
|
|
Author |
Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Back to Front Architecture for Diagnosis as a Service |
Type |
Conference Article |
|
Year |
2018 |
Publication |
20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
343-346 |
|
|
Keywords |
|
|
|
Abstract |
Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction. |
|
|
Address |
Timisoara; Rumania; September 2018 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
SYNASC |
|
|
Notes |
IAM; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SVA2018 |
Serial |
3360 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Guillermo Torres |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
Type |
Conference Article |
|
Year |
2020 |
Publication |
34th International Congress and Exhibition on Computer Assisted Radiology & Surgery |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; June 2020 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CARS |
|
|
Notes |
IAM; 600.139; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GiT2020 |
Serial |
3472 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Women in Geometry and Topology |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Barcelona; September 2019 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; DAG; 600.139; 600.145; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GRP2020 |
Serial |
3473 |
|
Permanent link to this record |
|
|
|
|
Author |
Esmitt Ramirez; Carles Sanchez; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Localizing Pulmonary Lesions Using Fuzzy Deep Learning |
Type |
Conference Article |
|
Year |
2019 |
Publication |
21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
290-294 |
|
|
Keywords |
|
|
|
Abstract |
The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. |
|
|
Address |
Timisoara; Rumania; September 2019 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
SYNASC |
|
|
Notes |
IAM; 600.145; 600.140; 601.337; 601.323 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RSG2019 |
Serial |
3531 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Mental Workload Detection Based on EEG Analysis |
Type |
Conference Article |
|
Year |
2021 |
Publication |
Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. |
Abbreviated Journal |
|
|
|
Volume |
339 |
Issue |
|
Pages |
268-277 |
|
|
Keywords |
Cognitive states; Mental workload; EEG analysis; Neural Networks. |
|
|
Abstract |
The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation. |
|
|
Address |
Virtual; October 20-22 2021 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CCIA |
|
|
Notes |
IAM; 600.139; 600.118; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ |
Serial |
3723 |
|
Permanent link to this record |
|
|
|
|
Author |
Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images |
Type |
Conference Article |
|
Year |
2023 |
Publication |
7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level. |
|
|
Address |
|
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
DIPMAI |
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ TRB2023 |
Serial |
3926 |
|
Permanent link to this record |
|
|
|
|
Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules |
Type |
Conference Article |
|
Year |
2023 |
Publication |
37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Pòster |
|
|
Address |
Munich; Germany; June 2023 |
|
|
Corporate Author ![sorted by Corporate Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CARS |
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ TGR2023a |
Serial |
3950 |
|
Permanent link to this record |