2021/06/29 by Liming Wu, Wu, Liming, Shuo Han +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #AI in cancer detection #Artificial intelligence #Cell Image Analysis Techniques #Centroid #Cluster (spacecraft) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #FOS: Electrical engineering #Fluorescence #Fluorescence microscope #Ground truth #Image and Video Processing (eess.IV) #Microscopy #Optics #Pattern recognition (psychology) #Physics #Segmentation #Spectroscopy Techniques in Biomedical and Chemical Research #Volume (thermodynamics) #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.15753
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/06/29 · openalex created_date 2021/07/05 · arxiv created 2021/11/04 · arxiv updated 2021/11/05 · openalex updated_date 2026/08/05
Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images. Existing automated nuclei localization methods face three main challenges: (1) Most of object detection methods work only on 2D images and are difficult to extend to 3D volumes; (2) Segmentation-based models can be used on 3D volumes but it is computational expensive for large microscopy volumes and they have difficulty distinguishing different instances of objects; (3) Hand annotated ground truth is limited for 3D microscopy volumes. To address these issues, we present a scalable approach for nuclei centroid detection of 3D microscopy volumes. We describe the RCNN-SliceNet to detect 2D nuclei centroids for each slice of the volume from different directions and 3D agglomerative hierarchical clustering (AHC) is used to estimate the 3D centroids of nuclei in a volume. The model was trained with the synthetic microscopy data generated using Spatially Constrained Cycle-Consistent Adversarial Networks (SpCycleGAN) and tested on different types of real 3D microscopy data. Extensive experimental results demonstrate that our proposed method can accurately count and detect the nuclei centroids in a 3D microscopy volume.