arXiv Open Access 2022

Nuclei instance segmentation and classification in histopathology images with StarDist

Martin Weigert Uwe Schmidt

Lihat Sumber

Abstrak

Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally developed for fluorescence microscopy, can be extended and successfully applied to histopathology images. This is substantiated by conducting experiments on the Lizard dataset, and through entering the Colon Nuclei Identification and Counting (CoNIC) challenge 2022, where our approach achieved the first spot on the leaderboard for the segmentation and classification task for both the preliminary and final test phase.

Topik & Kata Kunci

cs.CV

Penulis (2)

Martin Weigert

Uwe Schmidt

Format Sitasi

APA MLA BibTeX

Weigert, M., Schmidt, U. (2022). Nuclei instance segmentation and classification in histopathology images with StarDist. https://arxiv.org/abs/2203.02284

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓