Ensemble Processing and Synthetic Image Generation For Abnormally Shaped Nuclei Segmentation
Han, Y.; Lei, Y.; Shkolnikov, V.; Xin, D.; Auduong, A.; Barcelo, S.; Delp, E. J.
Show abstract
Abnormalities in biological cell nuclei morphology are correlated with cell cycle stages, disease states, and various external stimuli. There have been many deep learning approaches that have described nuclei segmentation and analysis of nuclear morphology. One problem with many deep learning methods is acquiring large amounts of annotated nuclei data, which is generally expensive to obtain. In this paper, we propose a system to segment abnormally shaped nuclei with a limited amount of training data. We first generate specific shapes of synthetic nuclei groundtruth. We randomly sample these synthetic groundtruth images into training sets to train several Mask R-CNNs. We design an ensemble strategy to combine or fuse segmentation results from the Mask R-CNNs. We also design an oval nuclei removal by StarDist to reduce the false positives and improve the overall segmentation performance. Our experiments indicate that our method outperforms other methods in segmenting abnormally shaped nuclei.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Complementary Performances of Convolutional and Capsule Neural Networks on Classifying Microfluidic Images of Dividing Yeast Cells 97%
- Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures 96%
- Caveolae and scaffold detection from single molecule localization microscopy data using deep learning 96%
Similar papers in this journal
Similar papers in this journal
- A Deep Learning approach for time-consistent cell cycle phase prediction from microscopy data 95%
- RETINA: Reconstruction-based Pre-Trained Enhanced TransUNet for Electron Microscopy Segmentation on the CEM500K Dataset 94%
- Teaching deep networks to see shape: Lessons from a simplified visual world. 94%
Similar papers in this journal
Similar papers in this journal
- Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation 97%
- A deep learning network for parallel self-denoising and segmentation in visible light optical coherence tomography of human retina 94%
- A Tailored Approach To Study Legionella Infection Using Lattice Light Sheet Microscope (LLSM) 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.