Root segmentation of horticultural plants in X-Ray CT images by integrating 2D instance segmentation with 3D point cloud clustering

X-ray computed tomography (CT) is a powerful tool for in situ plant root system architecture (RSA) characterization. Accurate root segmentation from CT images is integral to studying RSA. Research studies on segmenting roots from CT images have been mainly limited to image processing-based approaches which may require parameter tuning and often lack common segmentation metrics, e.g., Dice and IoU. A recent deep learning approach utilizes a volumetric encoder-decoder network to achieve a high Dice score and IoU. However, training a volumetric model is dependent on the availability of fully annotated scans of the growing medium column, obtaining which can be time-consuming, tedious, and resource intensive. In this study, an efficient method using deep learning-based instance segmentation in conjunction with density-based spatial clustering of applications with noise (DBSCAN)-based filtering was developed and evaluated for two horticultural plant species. A pretrained Mask R-CNN model was fine-tuned on images selected along different axes of the three-dimensional scans to identify the best view selection strategy for volumetric root segmentation. DBSCAN was used to filter noise from the volumetric segmentation with an automated parameter tuning technique. The proposed method was evaluated on scans of poinsettias and onions and achieved best average scores of 0.831, 0.839, 0.834, and 0.718 for Precision, Recall, Dice, and IoU, respectively. Further experiments showed reducing the training data to 1 % did not significantly impact the segmentation accuracy. Therefore, the proposed method has promising potential to facilitate RSA analysis with its high utility.