Identify the cells' nuclei based on the deep learning neural network
This work addresses the need for accurate cell nuclei localization in medical analysis to assist doctors, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of automatically identifying cell nuclei in medical images by comparing three deep learning frameworks (Mask RCNN, U-net, and DenseUNet) on the Data Science Bowl 2018 dataset, achieving good performance without ensemble methods.
Identify the cells' nuclei is the important point for most medical analyses. To assist doctors finding the accurate cell' nuclei location automatically is highly demanded in the clinical practice. Recently, fully convolutional neural network (FCNs) serve as the back-bone in many image segmentation, like liver and tumer segmentation in medical field, human body block in technical filed. The cells' nuclei identification task is also kind of image segmentation. To achieve this, we prefer to use deep learning algorithms. we construct three general frameworks, one is Mask Region-based Convolutional Neural Network (Mask RCNN), which has the high performance in many image segmentations, one is U-net, which has the high generalization performance on small dataset and the other is DenseUNet, which is mixture network architecture with Dense Net and U-net. we compare the performance of these three frameworks. And we evaluated our method on the dataset of data science bowl 2018 challenge. For single model without any ensemble, they all have good performance.