What is Mask-RCNN based pathology image segmentation?

Mask Regional Convolutional Neural Network (Mask-RCNN) is a newly developed deep-learning algorithm. We trained a Mask-RCNN model to segment tumor nuclei, stroma nuclei, lymphocyte nuclei, macrophage nuclei, karyorrhexis, and red blood cells in pathological Hematoxylin & Eosin (HE) stained images. Around 10,000 cells are covered in our training dataset from the National Lung Screening Trial (NLST) cohort. This tool aims to dissect tumor microenvironment in cell level.

1.Upload

Prepare your pathological image file.

2.Analyzing

Analyze user's image using Mask-RCNN based pathology image segmentation model.

3.Results

Visualize the results and allow the user to download.

How to use Mask-RCNN based pathology image segmentation?

The design of this segmentation online tool aims to provide a simply and user-friendly usage environment. The user only needs to prepare a pathological image to submit. We suggest the user input the email to receive job completion alert because the job execution might take several minutes. Once the job is completed, the original image and the segmented results will be displayed together on this website.

Examples of Online Analysis

# Original Processed
Original Image Mask Image Overlapped Image
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Color Annotation