GRUU-Net

• Verfasst von: Wollmann, Thomas [VerfasserIn]
• Verfasst von: Gunkel, Manuel [VerfasserIn]
• Verfasst von: Erfle, Holger [VerfasserIn]
• Verfasst von: Rippe, Karsten [VerfasserIn]
• Verfasst von: Rohr, Karl [VerfasserIn]
• Titel: GRUU-Net
• Titelzusatz: Integrated convolutional and gated recurrent neural network for cell segmentation
• Verf.angabe: T. Wollmann, M. Gunkel, I. Chung, H. Erfle, K. Rippe, K. Rohr
• E-Jahr: 2019
• Jahr: 31 May 2019
• Umfang: 12 S.
• Fussnoten: Gesehen am 21.10.2019
• Titel Quelle: Enthalten in: Medical image analysis
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1996
• Jahr Quelle: 2019
• Band/Heft Quelle: 56(2019), Seite 68-79
• ISSN Quelle: 1361-8423
• DOI: doi:10.1016/j.media.2019.04.011
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Convolutional neural network
• Sach-SW: Deep learning
• Sach-SW: Gated Recurrent Unit
• Sach-SW: Microscopy
• Sach-SW: Segmentation
• K10plus-PPN: 1679240366

Abstract

Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks. In this work, we propose a new deep learning method for cell segmentation, which integrates convolutional neural networks and gated recurrent neural networks over multiple image scales to exploit the strength of both types of networks. To increase the robustness of the training and improve segmentation, we introduce a novel focal loss function. We also present a distributed scheme for optimized training of the integrated neural network. We applied our proposed method to challenging data of glioblastoma cell nuclei and performed a quantitative comparison with state-of-the-art methods. Insights on how our extensions affect training and inference are also provided. Moreover, we benchmarked our method using a wide spectrum of all 22 real microscopy datasets of the Cell Tracking Challenge.