Publications

@inproceedings{Zeilmann2024KI,
	author = {Zeilmann, Alexander and Heuveline, Vincent},
	year = {2024},
	month = {sep 26},
	title = {KI-{Morph} -- {User}-friendly large-scale image analysis & {AI} on {bwHPC} systems},
	howpublished = {https://ki-morph.de/publications/Zeilmann\textunderscore{}-\textunderscore{}KI-Morph\textunderscore{}--\textunderscore{}User-friendly\textunderscore{}large-scale\textunderscore{}image\textunderscore{}analysis\textunderscore{}&\textunderscore{}AI\textunderscore{}on\textunderscore{}bwHPC\textunderscore{}systems\textunderscore{}-\textunderscore{}Preprint.pdf},
}

@inproceedings{Zeilmann2024KI,
	abstract = {Artificial Intelligence (AI) has become indispensable for analyzing large-scale datasets, particularly in the realm of 3D image volumes. However, effectively harnessing AI for such tasks often requires advanced algorithms and high-performance computing (HPC) resources, presenting significant challenges for non-technical users. To overcome these barriers, we present KI-Morph, a novel software platform tailored for large-scale image analysis on the bwHPC infrastructure. Our goal is to help researchers analyzing 3D image data as efficiently as possible. To this end, KI-Morph offers a user-friendly interface that seamlessly integrates with HPC resources, enabling sophisticated AI-driven analysis without requiring deep technical expertise in either AI or HPC. The platform prioritizes data privacy and sovereignty, ensuring that users retain full control over their data. Additionally, the components developed for KI-Morph support researchers not only with advanced data analysis but also with science outreach and communication by enabling the creation of interactive online visualizations, for example using the 2D, 3D and augmented reality viewers.},
	author = {Zeilmann, Alexander and Heuveline, Vincent},
	doi = {},
	date = {2024-09-26},
	language = {de},
	pubstate = {submitted},
	title = {KI-{Morph} -- {User}-friendly large-scale image analysis & {AI} on {bwHPC} systems},
	url = {https://ki-morph.de/publications/Zeilmann_-_KI-Morph_–_User-friendly_large-scale_image_analysis_&_AI_on_bwHPC_systems_-_Preprint.pdf},
}

@article{Martinez2024Turbinal,
	author = {Martinez, Quentin and Amson, Eli and Ruf, Irina and Smith, Timothy D. and Pirot, Nelly and Broyon, Morgane and Lebrun, Renaud and Captier, Guillaume and Gasc{\' o} Mart{\' i}n, Cristina and Ferreira, Gabriel and Fabre, PierreHenri},
	journal = {Biological Reviews},
	year = {2024},
	month = {aug 2},
	publisher = {Wiley},
	title = {Turbinal bones are still one of the last frontiers of the tetrapod skull: hypotheses, challenges and perspectives},
}

@article{Martinez2024Turbinal,
	abstract = {Turbinals are bony or cartilaginous structures that are present in the nasal cavity of most tetrapods. They are involved in key functions such as olfaction, heat, and moisture conservation, as well as protection of the respiratory tract. Despite recent studies that challenged longstanding hypotheses about their physiological and genomic correlation, turbinals remain largely unexplored, particularly for nonmammalian species. Herein, we review and synthesise the current knowledge of turbinals using an integrative approach that includes comparative anatomy, physiology, histology and genomics. In addition, we provide synonyms and correspondences of tetrapod turbinals from about 80 publications. This work represents a first step towards drawing hypotheses of homology for the whole clade, and provides a strong basis to develop new research avenues.},
	author = {Martinez, Quentin and Amson, Eli and Ruf, Irina and Smith, Timothy D. and Pirot, Nelly and Broyon, Morgane and Lebrun, Renaud and Captier, Guillaume and Gasc{\' o} Mart{\' i}n, Cristina and Ferreira, Gabriel and Fabre, PierreHenri},
	journaltitle = {Biological Reviews},
	shortjournal = {Biological Reviews},
	doi = {10.1111/brv.13122},
	issn = {1464-7931},
	date = {2024-08-02},
	language = {en},
	publisher = {Wiley},
	title = {Turbinal bones are still one of the last frontiers of the tetrapod skull: hypotheses, challenges and perspectives},
	url = {http://dx.doi.org/10.1111/brv.13122},
}

@article{Martinez2024Disparity,
	author = {Martinez, Quentin and Wright, Mark and Dubourguier, Benjamin and Ito, Kai and van de Kamp, Thomas and Hamann, Elias and Zuber, Marcus and Ferreira, Gabriel and Blanc, R{\' e}mi and Fabre, PierreHenri and Hautier, Lionel and Amson, Eli},
	journal = {The Anatomical Record},
	year = {2024},
	month = {aug 5},
	publisher = {Wiley},
	title = {Disparity of turbinal bones in placental mammals},
}

@article{Martinez2024Disparity,
	abstract = {Turbinals are key bony elements of the mammalian nasal cavity, involved in heat and moisture conservation as well as olfaction. While turbinals are well known in some groups, their diversity is poorly understood at the scale of placental mammals, which span 21 orders. Here, we investigated the turbinal bones and associated lamellae for one representative of each extant order of placental mammals. We segmented and isolated each independent turbinal and lamella and found an important diversity of variation in the number of turbinals, as well as their size, and shape. We found that the turbinal count varies widely, from zero in the La Plata dolphin, (\textit{Pontoporia blainvillei}) to about 110 in the African bush elephant (\textit{Loxodonta africana}). Multiple turbinal losses and additional gains took place along the phylogeny of placental mammals. Some changes are clearly attributed to ecological adaptation, while others are probably related to phylogenetic inertia. In addition, this work highlights the problem of turbinal nomenclature in some placental orders with numerous and highly complex turbinals, for which homologies are extremely difficult to resolve. Therefore, this work underscores the importance of developmental studies to better clarify turbinal homology and nomenclature and provides a standardized comparative framework for further research.},
	author = {Martinez, Quentin and Wright, Mark and Dubourguier, Benjamin and Ito, Kai and van de Kamp, Thomas and Hamann, Elias and Zuber, Marcus and Ferreira, Gabriel and Blanc, R{\' e}mi and Fabre, PierreHenri and Hautier, Lionel and Amson, Eli},
	journaltitle = {The Anatomical Record},
	shortjournal = {The Anatomical Record},
	doi = {10.1002/ar.25552},
	issn = {1932-8486},
	date = {2024-08-05},
	language = {en},
	publisher = {Wiley},
	title = {Disparity of turbinal bones in placental mammals},
	url = {http://dx.doi.org/10.1002/ar.25552},
}

@inproceedings{Richling2022Managing,
	author = {Richling, Sabine and Siebler, Sven and Balz, Alexander and K{\" u}hl, Robert and Baumann, Martin},
	year = {2022},
	month = {apr 21},
	organization = {heiBOOKS},
	title = {Managing large research data with {SDS}@hd},
}

@inproceedings{Richling2022Managing,
	abstract = {The scientific data storage SDS@hd is a central storage service for hot large-scale scientific data that can be used by researchers from all universities in Baden-W{\" u}rttemberg. It offers fast and secure file system storage capabilities for individuals and groups. The service is operated by the Heidelberg University Computing Centre and running in production since 2017 with a continuously growing number of users and storage projects. Access management can be done via a predefined set of roles and also based on access control lists on the filesystem level enabling researchers to share data in a collaborative fashion.},
	author = {Richling, Sabine and Siebler, Sven and Balz, Alexander and K{\" u}hl, Robert and Baumann, Martin},
	doi = {10.11588/HEIBOOKS.979.C13759},
	date = {2022-04-21},
	language = {de},
	publisher = {heiBOOKS},
	title = {Managing large research data with {SDS}@hd},
	url = {https://books.ub.uni-heidelberg.de//heibooks/catalog/book/979/chapter/13759},
}

@inproceedings{Schnetter2023bwVisu,
	author = {Schnetter, Erik and Beretta, Carlo Antonio and Baumann, Martin and Richling, Sabine and Heuschkel, Florian and Kuner, Thomas},
	year = {2023},
	organization = {heiBOOKS},
	title = {bwVisu: A {Scalable} {Remote} {Service} for {Interactive} {Data} {Processing} and {Training} for {Scientists}},
}

@inproceedings{Schnetter2023bwVisu,
	author = {Schnetter, Erik and Beretta, Carlo Antonio and Baumann, Martin and Richling, Sabine and Heuschkel, Florian and Kuner, Thomas},
	doi = {10.11588/HEIBOOKS.1288.C18073},
	date = {2023},
	language = {de},
	publisher = {heiBOOKS},
	title = {bwVisu: A {Scalable} {Remote} {Service} for {Interactive} {Data} {Processing} and {Training} for {Scientists}},
	url = {https://books.ub.uni-heidelberg.de//heibooks/catalog/book/1288/chapter/18073},
}

@article{Losel2020Introducing,
	author = {L{\" o}sel, Philipp D. and van de Kamp, Thomas and Jayme, Alejandra and Ershov, Alexey and Farag{\' o}, Tom{\' a}{\v s} and Pichler, Olaf and Tan Jerome, Nicholas and Aadepu, Narendar and Bremer, Sabine and Chilingaryan, Suren A. and Heethoff, Michael and Kopmann, Andreas and Odar, Janes and Schmelzle, Sebastian and Zuber, Marcus and Wittbrodt, Joachim and Baumbach, Tilo and Heuveline, Vincent},
	journal = {Nature Communications},
	number = {1},
	year = {2020},
	month = {nov 4},
	publisher = {{Springer Science and Business Media LLC}},
	title = {Introducing {Biomedisa} as an open-source online platform for biomedical image segmentation},
	volume = {11},
}

@article{Losel2020Introducing,
	abstract = {\textless{}jats:title\textgreater{}Abstract\textless{}/jats:title\textgreater{}\textless{}jats:p\textgreater{}We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.\textless{}/jats:p\textgreater{}},
	author = {L{\" o}sel, Philipp D. and van de Kamp, Thomas and Jayme, Alejandra and Ershov, Alexey and Farag{\' o}, Tom{\' a}{\v s} and Pichler, Olaf and Tan Jerome, Nicholas and Aadepu, Narendar and Bremer, Sabine and Chilingaryan, Suren A. and Heethoff, Michael and Kopmann, Andreas and Odar, Janes and Schmelzle, Sebastian and Zuber, Marcus and Wittbrodt, Joachim and Baumbach, Tilo and Heuveline, Vincent},
	journaltitle = {Nature Communications},
	shortjournal = {Nat Commun},
	doi = {10.1038/s41467-020-19303-w},
	issn = {2041-1723},
	number = {1},
	date = {2020-11-04},
	language = {en},
	publisher = {{Springer Science and Business Media LLC}},
	title = {Introducing {Biomedisa} as an open-source online platform for biomedical image segmentation},
	url = {http://dx.doi.org/10.1038/s41467-020-19303-w},
	volume = {11},
}