Publications using BiaPy

LĂłpez-Cano, Daniel, et al. "Characterizing structure formation through instance
segmentation." arXiv preprint arXiv:2311.12110 (2023).

Franco-Barranco, Daniel, et al. "Current Progress and Challenges in Large-scale 3D
Mitochondria Instance Segmentation." IEEE transactions on medical imaging (2023).

BackovĂĄ, Lenka, et al. "Modeling Wound Healing Using Vector Quantized Variational
Autoencoders and Transformers." 2023 IEEE 20th International Symposium on Biomedical
Imaging (ISBI). IEEE, 2023.

Andres-San Roman, Jesus A., et al. "CartoCell, a high-content pipeline for 3D image
analysis, unveils cell morphology patterns in epithelia." Cell Reports Methods 3.10 (2023).

Franco-Barranco, Daniel, et al. "Deep learning based domain adaptation for mitochondria
segmentation on EM volumes." Computer Methods and Programs in Biomedicine 222 (2022):
106949.

Franco-Barranco, Daniel, Arrate Muñoz-Barrutia, and Ignacio Arganda-Carreras. "Stable
deep neural network architectures for mitochondria segmentation on electron microscopy
volumes." Neuroinformatics 20.2 (2022): 437-450.

Wei, Donglai, et al. "Mitoem dataset: Large-scale 3d mitochondria instance segmentation
from em images." International Conference on Medical Image Computing and Computer-Assisted
Intervention. Cham: Springer International Publishing, 2020.

References

[1]

Lenka BackovĂĄ, Guillermo Bengoetxea, Svana Rogalla, Daniel Franco-Barranco, JĂ©rĂŽme Solon, and Ignacio Arganda-Carreras. Modeling wound healing using vector quantized variational autoencoders and transformers. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2023.

[2]

Elvan Boke and others. 3D Mapping of Intact Ovaries Reveals the Aging Dynamics of the Ovarian Reserve. bioRxiv, 2025. doi:10.1101/2025.11.07.686728.

[3]

D. Franco-Barranco, J. Pastor-Tronch, A. Gonzålez-Marfil, A. Muñoz-Barrutia, and I. Arganda-Carreras. Deep learning based domain adaptation for mitochondria segmentation on em volumes. Computer Methods and Programs in Biomedicine, 222:106949, 2022.

[4]

Daniel Franco-Barranco, Arrate Muñoz-Barrutia, and Ignacio Arganda-Carreras. Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes. Neuroinformatics, Dec 2021. URL: https://doi.org/10.1007/s12021-021-09556-1, doi:10.1007/s12021-021-09556-1.

[5]

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 16000–16009. 2022.

[6]

Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2129–2137. 2019.

[7]

Manan Lalit, Pavel Tomancak, and Florian Jug. Embedseg: embedding-based instance segmentation for biomedical microscopy data. Medical image analysis, 81:102523, 2022.

[8]

Zudi Lin, Donglai Wei, Mariela D. Petkova, Yuelong Wu, Zergham Ahmed, Krishna Swaroop K, Silin Zou, Nils Wendt, Jonathan Boulanger-Weill, Xueying Wang, Nagaraju Dhanyasi, Ignacio Arganda-Carreras, Florian Engert, Jeff Lichtman, and Hanspeter Pfister. Nucmm dataset: 3d neuronal nuclei instance segmentation at sub-cubic millimeter scale. 2021. arXiv:2107.05840.

[9]

Pablo Ruiz-Amezcua, Daniel Franco-Barranco, David Reigada, Teresa Muñoz-Galdeano, Rodrigo M. Maza, and Manuel Nieto-DĂ­az. SpineDL: a new tool for the segmentation of neuron and anatomical structures in immunofluorescence images of damaged spinal cords. bioRxiv, pages 2025–11, 2025.

[10]

Uwe Schmidt, Martin Weigert, Coleman Broaddus, and Gene Myers. Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 265–273. Springer, 2018.

[11]

Adam L Tyson, Charly V Rousseau, Christian J Niedworok, Sepiedeh Keshavarzi, Chryssanthi Tsitoura, Lee Cossell, Molly Strom, and Troy W Margrie. A deep learning algorithm for 3d cell detection in whole mouse brain image datasets. PLoS computational biology, 17(5):e1009074, 2021.

[12]

Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni. Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data, 10(1):41, 2023.

[13]

X. Zhou, D. Wang, and P. KrĂ€henbĂŒhl. Objects as points. arXiv preprint arXiv:1904.07850, 2019.