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1. PIXEL-WISE NEURAL CELL INSTANCE SEGMENTATION

   Yi, J. ; Wu, P. ; Hoeppner, D. J. ; Metaxas, D. ( April 2018 , 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018))

   Accurate cell instance segmentation plays an important role in the study of neural cell interactions, which are critical for understanding the development of brain. These interactions are performed through the filopodia and lamellipodia of neural cells, which are extremely tiny structures and as a result render most existing instance segmentation methods powerless to precisely capture them. To solve this issue, in this paper we present a novel hierarchical neural network comprising object detection and segmentation modules. Compared to previous work, our model is able to efficiently share and make full use of the information at different levels between the twomore »modules. Our method is simple yet powerful, and experimental results show that it captures the contours of neural cells, especially the filopodia and lamellipodia, with high accuracy, and outperforms recent state of the art by a large margin.« less
2. Fast Neural Cell Detection Using Light-Weight SSD Neural Network

   Yi, J. ; Wu, P. ; Hoeppner, D. J. ; Metaxas, D. ( July 2017 , CVPR Workshop)

   Identifying the lineage path of neural cells is critical for understanding the development of brain. Accurate neural cell detection is a crucial step to obtain reliable delineation of cell lineage. To solve this task, in this paper we present an efficient neural cell detection method based on SSD (single shot multibox detector) neural network model. Our method adapts the original SSD architecture and removes the un- necessary blocks, leading to a light-weight model. More- over, we formulate the cell detection as a binary regression problem, which makes our model much simpler. Experimen- tal results demonstrate that, with only a smallmore »training set, our method is able to accurately capture the neural cells under severe shape deformation in a fast way.« less
3. EDLT: Enabling Deep Learning for Generic Data Classification

   https://doi.org/10.1109/ICDM.2018.00030  

   Han, Huimei ; Zhu, Xingquan ; Li, Ying ( November 2018 , Proc. of the 18th IEEE International Conference on Data Mining (ICDM- 2018))

   This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instance feature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix , we use 0/1 optimization to reorder features such that the ones withmore »strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification« less
4. Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes

   Jingru Yi, Pengxiang Wu ( October 2019 , MICCAI 2019)

   Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells due to the lack of global understanding of the objects. In contrast, box-based instance segmentation solves this problem by combining object detection with segmentation. However, existing methods typically utilize anchor box-based detectors, which would lead to inferior instance segmentation performance due to the class imbalance issue. In this paper, we propose a new box-based cell instance segmentation method. In particular, we first detect the five pre-defined points of a cell via keypoints detection. Then we group thesemore »points according to a keypoint graph and subsequently extract the bounding box for each cell. Finally, cell segmentation is performed on feature maps within the bounding boxes. We validate our method on two cell datasets with distinct object shapes, and empirically demonstrate the superiority of our method compared to other instance segmentation techniques.« less
5. CONTEXT-REFINED NEURAL CELL INSTANCE SEGMENTATION

   Yi, J. ; Wu, P. ; Huang, Q. ; Qu, H. ; Hoeppner, D. J. ; Metaxas, D. ( April 2019 , 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019))

   Neural cell instance segmentation serves as a valuable tool for the study of neural cell behaviors. In general, the instance segmentation methods compute the region of interest (ROI) through a detection module, where the segmentation is sub- sequently performed. To precisely segment the neural cells, especially their tiny and slender structures, existing work em- ploys a u-net structure to preserve the low-level details and encode the high-level semantics. However, such method is insufficient for differentiating the adjacent cells when large parts of them are included in the same cropped ROI. To solve this problem, we propose a context-refined neural cellmore »instance segmentation model that learns to suppress the back- ground information. In particular, we employ a light-weight context refinement module to recalibrate the deep features and focus the model exclusively on the target cell within each cropped ROI. The proposed model is efficient and accurate, and experimental results demonstrate its superiority com- pared to the state-of-the-arts.« less