Deep learning based domain adaptation for mitochondria segmentation on EM volumes.

[EN] BACKGROUND AND OBJECTIVE: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species. METHODS: We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation. RESULTS: We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets. CONCLUSIONS: The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.I. Arganda-Carreras would like to acknowledge the support of the 2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. This work is supported in part by the University of the Basque Country UPV/EHU grant GIU19/027 and by Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB-I00, MCIN/AEI /10.13039/501100011033/, cofinanced by European Regional Development Fund (ERDF), “A way of making Europe.

Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

[EN] Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th-90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 +/- 8.34% and 90.2 +/- 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th-90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR); by the Generalitat Valenciana (AICO/2019/220).Prats-Boluda, G.; Pastor-Tronch, J.; Garcia-Casado, J.; Monfort-Ortiz, R.; Perales Marín, A.; Diago, V.; Roca Prats, A.... (2021). Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography. Sensors. 21(7):1-18. https://doi.org/10.3390/s21072496S11821

Desarrollo de un sistema de predicción del parto inminente en mujeres con amenaza de parto prematuro mediante algoritmos computacionalmente eficientes:valoración del desempeño de random forest y/o extreme learning machine

[ES] La amenaza de parto prematuro (APP) es una de las principales causas de hospitalización en todo el mundo durante la segunda etapa del embarazo e implica una serie de tratamientos de elevado coste y cierto riesgo tanto para la madre como para el feto. A pesar de que las causas y factores de riesgo del parto pretérmino todavía siguen siendo estudiados, numerosos estudios han establecido la existencia de variaciones significativas en los registros de electrohisterografía conforme se acerca el parto, enfocando la atención en su uso como método diagnóstico ante estas APP. El objetivo del siguiente estudio es desarrollar un sistema de predicción del parto inminente en mujeres con amenaza de parto prematuro por medio de una serie de algoritmos computacional- mente eficientes en base a una serie de parámetros extraidos de los registros de EHG junto con parámetros obstétricos. Para ello, se ha comparado el desempeño de diferentes algoritmos de clasificación como Árboles de decisión, Random Forest, KNN y Extreme Learning Machine (ELM) así como su optimización en base al F1-Score y sensibilidad. Además se ha estudiado el efecto de la inclusión de parámetros obstétricos y de las técnicas de reducción de dimensionalidad como la PCA en el desempeño de estos clasificadores. Los resultados muestran la superioridad de ELM y KNN como algoritmos de clasificación propor- cionando los mejores resultados en validación con una buena generalización a los datos de test, con unos resultados de F1-Score promedio en el grupo de test de 84.96 % y 84.62 % respectivamente y una sensibilidad promedio de 91.89 % y 98 %. También se ha demostrado como no existen dife- rencias estadísticamente significativas entre el uso de F1-Score y sensibilidad de cara a optimizar los hiperparámetros y el desempeño de los clasificadores. Por último, la PCA ha mostrado una mejora en el desempeño al ser aplicada junto con ELM mientras que el uso de datos obstétricos no ha proporcionado ningún tipo de mejora en las métricas de nuestros clasificadores. Estos re- sultados sugieren la posibilidad de un método de screening y diagnóstico escalable a la práctica clínica en base a registros de EHG ante una amenaza de parto prematuro de cara a optimizar recursos hospitalarios y mejorar el bienestar materno-fetal evitando hospitalizaciones innecesarias y los riesgos asociados a un mal tratamiento.[EN] The threat of preterm labour is one of the main causes of hospitalization throughout the world during the second stage of pregnancy and involves a series of high cost treatments and some risk for both the mother and the fetus. Despite the fact that the causes and risk factors of preterm birth are still being studied, numerous studies have established the existence of significant variations in the records of electrohysterography as delivery approaches, focusing attention on its use as a diagnostic method for these threats. The purpose of the following study is to develop a prediction system in order to predict imminent delivery in women under threaten of preterm birth by means of a series of computationally efficient algorithms based on parameters extracted from the EHG records together with obstetric ones. For this purpose different classification algorithms such as Decision Trees, Random Forest, KNN and Extreme Learning Machine (ELM) have been compared, as well as their optimization pro- cedure based on the F1-Score and sensitivity. Moreover, it has also been tested the effect of the inclusion of obstetric parameters and dimensionality reduction techniques such as PCA in the performance of these algorithms. The results show the superiority of ELM and KNN as classification algorithms, providing the best results in validation with a good generalization to the test data, they have provided an average F1-Score results in the test group of 84.96 % and 84.62 % respectively and an average sensitivity of 91.89 % and 98 %. It has also been demonstrated how the use of F1-Score and sensitivity in order to optimize the hyper-parameters do not report any statistically significant difference between them regarding the performance of the classifiers. Finally, PCA has shown an improvement in performance when applied together with ELM, whereas the use of obstetric data has not provided any type of enhacement in the metrics of our classifiers. These results suggest the possibility of a diagnostic method scalable to clinical practice based on EHG records in the event of a preterm-birth threat in order to optimize hospital resources and improve maternal-fetal well-being, avoiding unnecessary hospitalizations and the risks associated with a wrong treatment.Pastor Tronch, J. (2020). Desarrollo de un sistema de predicción del parto inminente en mujeres con amenaza de parto prematuro mediante algoritmos computacionalmente eficientes:valoración del desempeño de random forest y/o extreme learning machine. http://hdl.handle.net/10251/156476TFG

Deep learning based domain adaptation for mitochondria segmentation on EM volumes

Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species. Methods: We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation. Results: We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets. Conclusions: The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.I. Arganda-Carreras would like to acknowledge the support of the 2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. This work is supported in part by the University of the Basque Country UPV/EHU grant GIU19/027 and by Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB-I00, MCIN/AEI /10.13039/501100011033/, cofinanced by European Regional Development Fund (ERDF), “A way of making Europe.