A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids

Autor: Julian Schröter, Luca Deininger, Blaz Lupse, Petra Richter, Steffen Syrbe, Ralf Mikut, Sabine Jung-Klawitter

Publikováno v: Scientific Data, Vol 11, Iss 1, Pp 1-10 (2024)

Abstract Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evalua

Externí odkaz: https://doaj.org/article/57735131f5a34b809ff9c2ea93c166ad

A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids.

Autor: Schröter J; Division of Pediatric Epileptology, Center for Pediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany., Deininger L; Division of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, University Hospital Heidelberg, Heidelberg, Germany.; Group for Automated Image and Data Analysis, Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany., Lupse B; Division of Pediatric Epileptology, Center for Pediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany., Richter P; Division of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, University Hospital Heidelberg, Heidelberg, Germany.; MSH Medical School Hamburg, University of Applied Sciences and Medical University, Hamburg, Germany., Syrbe S; Division of Pediatric Epileptology, Center for Pediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany., Mikut R; Group for Automated Image and Data Analysis, Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany. ralf.mikut@kit.edu., Jung-Klawitter S; Division of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, University Hospital Heidelberg, Heidelberg, Germany. Sabine.Jung-Klawitter@med.uni-heidelberg.de.

Publikováno v: Scientific data [Sci Data] 2024 May 20; Vol. 11 (1), pp. 514. Date of Electronic Publication: 2024 May 20.

Segment Anything Model and Fully Convolutional Data Description for Plant Multi-Disease Detection on Field Images

Autor: Emmanuel Moupojou, Florent Retraint, Hyppolite Tapamo, Marcellin Nkenlifack, Cheikh Kacfah, Appolinaire Tagne

Publikováno v: IEEE Access, Vol 12, Pp 102592-102605 (2024)

Researchers have designed various models trained on public or private datasets for plant disease detection to help farmers remedy crop yield losses on their farms due to plant diseases. Plantvillage is the most widely used plant disease dataset with

Externí odkaz: https://doaj.org/article/c0c51f4024e74f40a016d79632ae85b1

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