TY - JOUR
T1 - Introducing Biomedisa as an open-source online platform for biomedical image segmentation
AU - Lösel, Philipp D.
AU - van de Kamp, Thomas
AU - Jayme, Alejandra
AU - Ershov, Alexey
AU - Faragó, Tomáš
AU - Pichler, Olaf
AU - Tan Jerome, Nicholas
AU - Aadepu, Narendar
AU - Bremer, Sabine
AU - Chilingaryan, Suren A.
AU - Heethoff, Michael
AU - Kopmann, Andreas
AU - Odar, Janes
AU - Schmelzle, Sebastian
AU - Zuber, Marcus
AU - Wittbrodt, Joachim
AU - Baumbach, Tilo
AU - Heuveline, Vincent
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85094968272&partnerID=8YFLogxK
U2 - 10.1038/s41467-020-19303-w
DO - 10.1038/s41467-020-19303-w
M3 - Article
SN - 2041-1723
VL - 11
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 5577
ER -