Grabber: A tool to improve convergence in interactive image segmentation

RootPainter: deep learning segmentation of biological images with corrective annotation

Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.

Dynamic Video Image Segmentation Based on Dual Channel Convolutional Kernel and Multi-Frame Feature Fusion

The color image of the fire hole is key for the working condition identification of the aluminum electrolysis cell (AEC). However, the image of the fire hole is difficult for image segmentation due to the nonuniform distributed illuminated background and oblique beam radiation. Thus, a joint dual channel convolution kernel (DCCK) and multi-frame feature fusion (MFF) method is developed to achieve dynamic fire hole video image segmentation. Considering the invalid or extra texture disturbances in the edge feature images, the DCCK is used to select the effective edge features. Since the obtained edge features of the fire hole are not completely closed, the MFF algorithm is further applied to complement the missing portion of the edge. This method can assist to obtain the complete fire hole image of the AEC. The experiment results demonstrate that the proposed method has higher precision, recall rate, and lower boundary redundancy rate with well segmented image edge for the aid of working condition identification of the AEC.