Self-training for Cell Segmentation and Counting

Luo, Junliang; Oore, Sageev; Hollensen, Paul; Fine, Alan; Trappenberg, Thomas

doi:10.1007/978-3-030-18305-9_37

Cited by 1 publication

(1 citation statement)

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“…Thorsten Falk developed a suite of software for cell detection, segmentation, counting, and morphometry using U-Net and 3D U-Net for easy use by more researchers [31]. J. Luo proposed an Expectation Maximization (EM)-like self-training method that makes it possible to train U-Net cell counting networks with a small number of samples [32]. Yue Guo combines U-Net and the Self-Attention module to propose a new network structure SAU-Net, which reduces the generalization gap for small datasets [33].…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Method for Counting Grains within a Panicle

Gong

Fan

2022

Machines

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The number of grains within a panicle is an important index for rice breeding. Counting manually is laborious and time-consuming and hardly meets the requirement of rapid breeding. It is necessary to develop an image-based method for automatic counting. However, general image processing methods cannot effectively extract the features of grains within a panicle, resulting in a large deviation. The convolutional neural network (CNN) is a powerful tool to analyze complex images and has been applied to many image-related problems in recent years. In order to count the number of grains in images both efficiently and accurately, this paper applied a CNN-based method to detecting grains. Then, the grains can be easily counted by locating the connected domains. The final error is within 5%, which confirms the feasibility of CNN-based method for counting grains within a panicle.

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Section: Introductionmentioning

confidence: 99%