Automatic evolutionary medical image segmentation using deformable models

International audienceThis paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed

Evidence evaluation in craniofacial superimposition using likelihood ratios

Financiado para publicación en acceso aberto: Universidad de Granada / CBUA[Abstract]: Craniofacial Superimposition is a forensic identification technique that supports decision-making when skeletal remains are involved. It is based on the analysis of the overlapping of a post-mortem skull with ante-mortem facial photographs. Despite its importance and wide applicability, the process remains complex and challenging. To address this, computerized methods have been proposed, but subjectivity and qualitative reporting persist in decision-making. This study introduces an evidence evaluation system proposal based on Likelihood Ratios, previously used in other forensic fields, such as DNA, voice, fingerprint, and facial comparison. We present a novel application of this framework to Craniofacial Superimposition. Our work comprises three experiments in which our LR system is trained and tested under distinct conditions concerning facial images: the first utilizes frontal facial photographs; the second employs lateral facial photographs; and the last one integrates both frontal and lateral facial photographs. In the three experiments, the proposed LR system stands out in terms of calibration and discriminating power, providing practitioners with a quantitative tool for evidence evaluation and integration. However, the lack of massive actual data obliged us to focus our study on synthetic data only. Therefore, it should be considered a proof of concept. Nevertheless, the resulting likelihood-ratio system offers objective decision support in Craniofacial Superimposition. Further studies are required to validate in a real scenario the conclusions achieved.This research has been developed within the R&D project CONFIA (grant PID2021-122916NB-I00), funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, and by grant FORAGE (B-TIC-456-UGR20) funded by Consejería de Universidad, Investigación e Innovación, both funded by “ERDF A way of making Europe”. Miss Martínez-Moreno is supported by grant PRE2022-102029 funded by MICIU/AEI/10.13039/501100011033 and the FSE+. Dr. Valsecchi's work is supported by Red.es under grant Skeleton-ID2.0 (2021/C005/00141299). Dr. Ibáñez's work is funded by the Spanish Ministry of Science, Innovation and Universities under grant RYC2020-029454-I and by Xunta de Galicia, Spain by grant ED431F 2022/21. The authors thank Pierre Guyomarc’h for providing us with the data used in this research. Funding for open access charge: Universidad de Granada / CBUA.Xunta de Galicia; ED431F 2022/21Junta de Andalucía; B-TIC-456-UGR2

Métricas sobre la robustez de soluciones en el problema TSALB ante la variación del mix de producción

Aplicación de métricas de robustez a configuraciones de línea en TSALBP - Ejemplo prototipo.Partiendo de la familia de modelos TSALBP (Time and Space Assembly Line Balancing Problem), proponemos diversas funciones para medir la robustez de un equilibrado de línea atendiendo a sus atributos temporales y espaciales. La versión robusta de TSALBP considera un conjunto de escenarios de demanda y presenta funciones que miden el exceso de carga, tanto temporal como espacial, en las estaciones de trabajo de la línea. Dichas funciones pueden emplearse como funciones objetivo en el problema de optimización resultante y como métricas ante un equilibrado de línea concreto; en ambos casos, la nueva versión de TSALBP pone a disposición del decisor nuevas soluciones de equilibrado más eficientes y robustas ante una demanda incierta.Preprin

Evidence evaluation in craniofacial superimposition using likelihood ratios

Craniofacial Superimposition is a forensic identification technique that supports decision-making when skeletal remains are involved. It is based on the analysis of the overlapping of a post-mortem skull with antemortem facial photographs. Despite its importance and wide applicability, the process remains complex and challenging. To address this, computerized methods have been proposed, but subjectivity and qualitative reporting persist in decision-making. This study introduces an evidence evaluation system proposal based on Likelihood Ratios, previously used in other forensic fields, such as DNA, voice, fingerprint, and facial comparison. We present a novel application of this framework to Craniofacial Superimposition. Our work comprises three experiments in which our LR system is trained and tested under distinct conditions concerning facial images: the first utilizes frontal facial photographs; the second employs lateral facial photographs; and the last one integrates both frontal and lateral facial photographs. In the three experiments, the proposed LR system stands out in terms of calibration and discriminating power, providing practitioners with a quantitative tool for evidence evaluation and integration. However, the lack of massive actual data obliged us to focus our study on synthetic data only. Therefore, it should be considered a proof of concept. Nevertheless, the resulting likelihood-ratio system offers objective decision support in Craniofacial Superimposition. Further studies are required to validate in a real scenario the conclusions achieved.R&D project CONFIA (grant PID2021-122916NB-I00), funded by MICIU/AEI/10.13039/ 501100011033 and by ERDF/EU - ‘‘ERDF A way of making Europe’’Grant FORAGE (B-TIC-456-UGR20) funded by Consejería de Universidad, Investigación e Innovación and by ‘‘ERDF A way of making Europe’’Miss Martínez-Moreno is supported by grant PRE2022-102029 funded by MICIU/AEI/10.13039/501100011033 and the FSE+Dr. Valsecchi’s work is supported by Red.es under grant Skeleton-ID2.0 (2021/C005/00141299)Dr. Ibáñez’s work is funded by the Spanish Ministry of Science, Innovation and Universities under grant RYC2020-029454-I and by Xunta de Galicia, Spain by grant ED431F 2022/21Funding for open access charge: Universidad de Granada / CBU

A First Approach to a Fuzzy Classification System for Age Estimation based on the Pubic Bone

The study of human remains suffers from a lack of information for determining a reliable estimation of the age of an individual. One of the most extended methods for this task was proposed in the twenties of the past century and is based on the analysis of the pubic bone. The method describes some age changes occurring in the pubic bone and establishes ten different age ranges with a description of the morphological aspect of the bone in each one of them. These descriptions are sometimes vague and there is not a systematic way for using the method. In this contribution we propose two different preliminary fuzzy rule-based classification system designs for age estimation from the pubic bone that consider the main morphological characteristics of the bone as independent and linguistic variables. So, we have identified the problem variables and we have defined the corresponding linguistic labels making use of forensic expert knowledge, that is also considered to design a decision support fuzzy system. A brief collection of pubic bones labeled by forensic anthropologists has been used for learning the second fuzzy rule-based classification system by means of a fuzzy decision tree. The experiments developed report a best performance of the latter approach

Fabrication and performance of low-fouling UF membranes for 2 the treatment of Isolated Soy Protein solutions

[EN] Consumers are becoming more conscious about the need to include functional and nutritional foods in their diet. This has increased the demand for food extracts rich in proteins and peptides with physiological effects that are used within the food and pharmaceutical industries. Among these protein extracts, soy protein and its derivatives are highlighted. Isolated soy protein (ISP) presents a protein content of at least 90%. Wastewaters generated during the production process contain small proteins (8-50 kDa), and it would be desirable to find a recovery treatment for these compounds. Ultrafiltration membranes (UF) are used for the fractionation and concentration of protein solutions. By the appropriate selection of the membrane pore size, larger soy proteins are retained and concentrated while carbohydrates and minerals are mostly recovered in the permeate. The accumulation and concentration of macromolecules in the proximity of the membrane surface generates one of the most important limitations inherent to the membrane technologies. In this work, three UF membranes based on polyethersulfone (PES) were fabricated. In two of them, polyethylene glycol (PEG) was added in their formulation to be used as a fouling prevention. The membrane fouling was evaluated by the study of flux decline models based on Hermia's mechanisms.The Universitat Politecnica de Valencia (Spain), through the project 2623 (PAID-05-10), funded this research.Garcia-Castello, EM.; Rodríguez López, AD.; Barredo Damas, S.; Iborra Clar, A.; Pascual-Garrido, J.; Iborra-Clar, MI. (2021). Fabrication and performance of low-fouling UF membranes for 2 the treatment of Isolated Soy Protein solutions. Sustainability. 13(24):1-16. https://doi.org/10.3390/su132413682S116132

Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning

Age estimation is a fundamental task in forensic anthropology for both the living and the dead. The procedure consists of analyzing properties such as appearance, ossification patterns, and morphology in different skeletonized remains. The pubic symphysis is extensively used to assess adults’ age-at-death due to its reliability. Nevertheless, most methods currently used for skeleton-based age estimation are carried out manually, even though their automation has the potential to lead to a considerable improvement in terms of economic resources, effectiveness, and execution time. In particular, explainable machine learning emerges as a promising means of addressing this challenge by engaging forensic experts to refine and audit the extracted knowledge and discover unknown patterns hidden in the complex and uncertain available data. In this contribution we address the automation of the decision making process of Todd’s pioneering age assessment method to assist the forensic practitioner in its application. To do so, we make use of the pubic bone data base available at the Physical Anthropology lab of the University of Granada. The machine learning task is significantly complex as it becomes an imbalanced ordinal classification problem with a small sample size and a high dimension. We tackle it with the combination of an ordinal classification method and oversampling techniques through an extensive experimental setup. Two forensic anthropologists refine and validate the derived rule base according to their own expertise and the knowledge available in the area. The resulting automatic system, finally composed of 34 interpretable rules, outperforms the state-of-the-art accuracy. In addition, and more importantly, it allows the forensic experts to uncover novel and interesting insights about how Todd’s method works, in particular, and the guidelines to estimate age-at-death from pubic symphysis characteristics, generally.Ministry of Science and Innovation, Spain (MICINN) Spanish GovernmentAgencia Estatal de Investigacion (AEI) PID2021-122916NB-I00 Spanish Government PGC2018-101216-B-I00Junta de AndaluciaUniversity of Granada P18 -FR -4262 B-TIC-456-UGR20European CommissionUniversidad de Granada/CBU

TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning

The combination of convolutional and recurrent neural networks is a promising framework. This arrangement allows the extraction of high-quality spatio-temporal features together with their temporal dependencies. This fact is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others. In this paper, the TSFEDL library is introduced. It compiles 22 state-of-the-art methods for both time series feature extraction and prediction, employing convolutional and recurrent deep neural networks for its use in several data mining tasks. The library is built upon a set of Tensorflow + Keras and PyTorch modules under the AGPLv3 license. The performance validation of the architectures included in this proposal confirms the usefulness of this Python package.This work has been partially supported by the Contract UGRAM OTRI-4260 and the Regional Government of Andalusia, under the program ‘‘Personal Investigador Doctor”, reference DOC_00235. This work was also supported by project PID2020-119478 GB-I00 granted by Ministerio de Ciencia, Innovación y Universidades, and projects P18-FR-4961 and P18-FR-4262 by Proyectos I + D+i Junta de Andalucia 2018