Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears

Leishmaniasis is a complex group of diseases caused by obligate unicellular and intracellular eukaryotic protozoa of the leishmania genus. Leishmania species generate diverse syndromes ranging from skin ulcers of spontaneous resolution to fatal visceral disease. These syndromes belong to three categories: visceral leishmaniasis, cutaneous leishmaniasis and mucosal leishmaniasis. The visceral leishmaniasis is based on the reticuloendothelial system producing hepatomegaly, splenomegaly and lymphadenopathy. In the present article, a semiautomatic segmentation strategy is proposed to obtain the segmentations of the evolutionary shapes of visceral leishmaniasis called parasites, specifically of the type amastigote and promastigote. For this purpose, the optical microscopy images containing said evolutionary shapes, which are generated from a blood smear, are subjected to a process of transformation of the color intensity space into a space of intensity in gray levels that facilitate their subsequent preprocessing and adaptation. In the preprocessing stage, smoothing filters and edge detectors are used to enhance the optical microscopy images. In a complementary way, a segmentation technique that groups the pixels corresponding to each one of the parasites, presents in the considered images, is applied. The results reveal a high correspondence between the available manual segmentations and the semi-automatic segmentations which are useful for the characterization of the parasites. The obtained segmentations let us to calculate areas and perimeters associated with the parasites segmented. These results are very important in clinical context where both the area and perimeter calculated are vital for monitoring the development of visceral leishmaniasis

PlasmoID: A dataset for Indonesian malaria parasite detection and segmentation in thin blood smear

Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large parasites are dominant, and the tiny parasites are suppressed. In addition, the amount and variance of data are important influences in establishing their models. In this study, we conduct two contributions. First, we collect 559 microscopic images containing 691 malaria parasites of thin blood smears. The dataset is named PlasmoID, and most data comes from rural Indonesia. PlasmoID also provides ground truth for parasite detection and segmentation purposes. Second, this study proposes a malaria parasite segmentation and detection scheme by combining Faster RCNN and a semantic segmentation technique. The proposed scheme has been evaluated on the PlasmoID dataset. It has been compared with recent studies of semantic segmentation techniques, namely UNet, ResFCN-18, DeepLabV3, DeepLabV3plus and ResUNet-18. The result shows that our proposed scheme can improve the segmentation and detection of malaria parasite performance compared to original semantic segmentation techniques

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 illion malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.The project is funded by the Microbiology Department of Vall d’Hebron Universitary Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and the Probitas FoundationPostprint (published version

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools : A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Deep learning; Malaria diagnosis; Microscopic examinationAprenentatge profund; Diagnòstic de malària; Examen microscòpicAprendizaje profundo; Diagnóstico de malaria; Examen microscópicoMalaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.The project is funded by the Microbiology Department of Vall d’Hebron Universitary Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and the Probitas Foundation

Analysis and parametrisation of an IBM of a Leishmania infantum in vitro culture

Leishmaniasis is a vector borne protozoan parasitic disease with the ninth highest burden amongst infectious diseases. There is a pressing need for the development of novel, improved drugs for its treatment. Currently, the knowledge of the mechanisms of action of the parasite and the drugs that treat it is still limited. As a consequence, drug research begins with in vitro screening. This makes an increase in the understanding of Leishmania infected macrophage cultures necessary for the improvement of drug screening methods. Aim: The purpose of this work is to parametrise an individual based model (IBM) of a Leishmania infantum and murine cell line RAW 264.7 in vitro culture. This process consists of the comparison of experimental results and the simulation outcome of the IBM. It is a necessary process in order to adjust the parameters of the model so that it gives the closest representation of the real system. On the one hand, this serves to shed light on the behaviour of this particular culture. On the other hand, the work proposes a mathematical methodology for parametrisation that is extensible to other cultures (different strains, cell lines, parasite species and medium composition). Experimental methods: The experimental design used to obtain the experimental data for the parametrisation of the model is described. The percentage of infected macrophages and the number of parasites per cell was determined at several time-points after infection. Parametrisation: The mathematical methodology for parametrisation is described and applied to this particular model. A preliminary analysis of the model was carried out in order to determine, for each parameter, the interval of values where simulation outcome and experimental results were most similar. The combination of these intervals, the parameter space, was then sampled using the Latin Hypercube Sampling (LHS) technique. Finally, the sampled combinations were implemented in the model and the outcome analysed. Results: On the one hand, a mathematical methodology for the parametrisation process was developed, extensible to different cultures. One the other hand, the combination that best reproduces the experimental results was determined. Conclusion: The development of the IBM has the potential to increase our understanding of the system and, therefore, lead to a more accurate approach in understanding and evaluating drug assays. However, the model still requires further expansion. In spite of the satisfactory results, there are still some improvements to be made to the parametrisation methodology.La leishmaniosi és una malaltia parasitària provocada per un protozou transmès per un vector. La seva incidència la situa com la novena malaltia infecciosa a nivell mundial. La necessitat de desenvolupar fàrmacs nous i millors és imperiosa. Actualment, encara, els coneixements sobre el mecanisme d'acció del paràsit i dels fàrmacs són limitats. Com a conseqüència, la recerca de nous fàrmacs s'inicia amb assajos in vitro. Això fa imprescindible per a la millora dels mètodes de triatge de fàrmacs una millor comprensió dels cultius de macròfags infectats per Leishmania. Objectiu: El propòsit d'aquest treball és parametritzar un model basat en l'individu (IBM) del cultiu in vitro de Leishmania infantum i la línia cel·lular murina RAW 264.7. Aquest procés implica la comparació de resultats experimentals amb les sortides del simulador i és necessari per a ajustar els paràmetres del model de manera que aquest representi amb màxima fidelitat el sistema real. Per una banda, serveix per a incrementar la comprensió del comportament d'aquest cultiu en particular. Per altra banda, el treball proposa una metodologia matemàtica per a la parametrització que pretén ser extensible a altres cultius (diferents soques, línies cel·lulars, espècies i composicions del medi). Mètode experimental: Es descriu el mètode experimental emprat per a obtenir les dades utilitzades per a la parametrització. Es determinen a diferents instants de temps el percentatge de macròfags infectats i el nombre de paràsits per cèl·lula. Parametrització: Es desenvolupa una metodologia matemàtica per a la parametrització i s'aplica a aquest model en particular. Un anàlisi preliminar permet establir en quins rangs de valors dels paràmetres la sortida del simulador s'assembla més a les dades experimentals. El conjunt d'aquests intervals, l'espai de paràmetres, és mostrejat amb la tècnica Latin Hypercube Sampling (LHS). Finalment, les combinacions de paràmetres mostrejades són implementades en el model i s'analitza el resultat. Resultats: Per una banda, s'ha desenvolupat una metodologia matemàtica per al procés de parametrització que és extensible a diferents cultius. Per altra banda, s'ha determinat la combinació de paràmetres mostrejats que millor reprodueix els resultats experimentals. Conclusions: El desenvolupament d'una metodologia per parametritzar models basats en l'individu té el potencial d'incrementar la nostra comprensió del sistema i, per tant, portarà a un enfocament més apropiat per a la comprensió i avaluació dels assajos de fàrmacs. Malgrat els bons resultats es constata que es poden realitzar, encara, algunes millores en la metodologia de parametrització.La leishmaniasis es una enfermedad parasitaria provocada por un protozoo transmitido por un vector. Su incidencia la sitúa como la novena enfermedad infecciosa a nivel mundial. La necesidad de desarrollar fármacos nuevos y mejores es imperante. Actualmente, los conocimientos sobre el mecanismo de acción del parásito y los fármacos son aún limitados. Como consecuencia, la investigación de nuevos fármacos empieza con ensayos in vitro. Es necesaria para la mejora de los métodos de elección de fármacos una mejor comprensión de los cultivos de macrófagos infectados por Leishmania. Objetivo: El propósito de este trabajo es parametrizar un modelo basa en el individuo (IBM) del cultivo in vitro de Leishmania infantum y la línea celular murina RAW 264.7. Este proceso implica la comparación de resultados experimentales con las salidas del simulador y es necesario para ajustar los parámetros del modelo de manera que este represente con máxima fidelidad el sistema real. Por un lado, sirve para incrementar la compresión del comportamiento de este cultivo en particular. Por otro lado, el trabajo propone una metodología matemática para la parametrización que pretende ser extensible a otros cultivos (diferentes cepas, líneas celulares, especies y composiciones del medio). Método experimental: Se describe el método experimental utilizado para obtener los datos usados para la parametrización. Se determinan a diferentes instantes de tiempo el porcentaje de macrófagos infectados y el número de parásitos por célula. Parametrización: Se desarrolla una metodología matemática para la parametrización y se aplica a este modelo en particular. Un análisis preliminar permite establecer en qué rangos de los valores de los parámetros la salida del simulador es más parecida a los datos experimentales. El conjunto de estos intervalos, el espacio de parámetros, se muestrea con la técnica Latin Hypercube Sampling (LHS). Finalmente, las combinaciones de parámetros muestreados son implementados en el modelo y su resultado es analizado. Resultados: Por un lado, se ha desarrollado una metodología matemática para el proceso de parametrización que es extensible a diferentes cultivos. Por otro lado, se ha determinado la combinación de parámetros muestreados que mejor reproduce los resultados experimentales. Conclusiones: El desarrollo de un IBM tiene el potencial de incrementar nuestra comprensión del sistema y, por tanto, llevará a un enfoque más apropiado para la comprensión y evaluación de los ensayos de fármacos. Sin embargo, el modelo requiere expansión. A pesar de los buenos resultados, se constata que se pueden realizar mejoras en la metodología de parametrización

Leishmaniasis parasite segmentation and classification using deep learning

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.Peer ReviewedPostprint (published version

Leishmaniasis parasite segmentation and classification using deep learning

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.Peer Reviewe