More Like this

1. Instance Segmentation of Neural Cells

   Yi, J. ; Wu, P. ; Jiang, M. ; Hoeppner, D. J. ; Metaxas, D. ( September 2018 , ECCV 2018 workshop)

   Instance segmentation of neural cells plays an important role in brain study. However, this task is challenging due to the special shapes and behaviors of neural cells. Existing methods are not precise enough to capture their tiny structures, e.g., filopodia and lamellipodia, which are critical to the understanding of cell interaction and behavior. To this end, we propose a novel deep multi-task learning model to jointly detect and segment neural cells instance-wise. Our method is built upon SSD, with ResNet101 as the backbone to achieve both high detection accuracy and fast speed. Furthermore, unlike existing works which tend to producemore »wavy and inaccurate boundaries, we embed a deconvolution module into SSD to better capture details. Experiments on a dataset of neural cell microscopic images show that our method is able to achieve better per- formance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods.« less
2. 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
3. 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
4. Attentive Normalization

   https://doi.org/10.1007/978-3-030-58520-4_5  

   Li, X. ; Sun, W. ; Wu, T. ( August 2020 , 16th European Conference on Computer Vision (2020))

   Vedaldi, A. (Ed.)

   In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way. The weights are learned by leveraging channel-wise feature attention. In experiments, we test the proposed AN using four representative neural architectures. In the ImageNet-1000 classification benchmark and the MS-COCO 2017more »object detection and instance segmentation benchmark. AN obtains consistent performance improvement for different neural architectures in both benchmarks with absolute increase of top-1 accuracy in ImageNet-1000 between 0.5\% and 2.7\%, and absolute increase up to 1.8\% and 2.2\% for bounding box and mask AP in MS-COCO respectively. We observe that the proposed AN provides a strong alternative to the widely used Squeeze-and-Excitation (SE) module. The source codes are publicly available at \href{https://github.com/iVMCL/AOGNet-v2}{the ImageNet Classification Repo} and \href{https://github.com/iVMCL/AttentiveNorm\_Detection}{the MS-COCO Detection and Segmentation Repo}.« less
5. Automated screening of sickle cells using a smartphone-based microscope and deep learning

   https://doi.org/10.1038/s41746-020-0282-y  

   de Haan, Kevin ; Ceylan Koydemir, Hatice ; Rivenson, Yair ; Tseng, Derek ; Van Dyne, Elizabeth ; Bakic, Lissette ; Karinca, Doruk ; Liang, Kyle ; Ilango, Megha ; Gumustekin, Esin ; et al ( May 2020 , npj Digital Medicine)

   Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2–0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementarymore »deep neural networks. The first neural network enhances and standardizes the blood smear images captured by the smartphone microscope, spatially and spectrally matching the image quality of a laboratory-grade benchtop microscope. The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear. These segmented images are then used to rapidly determine the SCD diagnosis per patient. We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.

   « less