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Easy-GT: Open-Source Software to Facilitate Making the Ground Truth for White Blood Cells Nucleus

arXiv 2101.11654 ↗First posted 27 Jan 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

The nucleus of white blood cells (WBCs) plays a significant role in their detection and classification. Appropriate feature extraction of the nucleus is necessary to fit a suitable artificial intelligence model to classify WBCs. Therefore, designing a method is needed to segment the nucleus accurately. There should be a comparison between the ground truths distinguished by a hematologist and the detected nuclei to evaluate the performance of the nucleus segmentation method accurately. It is a time-consuming and tedious task for experts to establish the ground truth manually. This paper presents an intelligent open-source software called Easy-GT to create the ground truth of WBCs' nucleus faster and easier. This software first detects the nucleus by employing a new Otsu's thresholding-based method with a dice similarity coefficient (DSC) of 95.42 %; the hematologist can then create a more accurate ground truth, using the designed buttons to modify the threshold value. This software can speed up ground truth's forming process more than six times.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 27 Jan 2021.

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References · 5

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  3. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  4. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  5. 2018Fully Automated Organ Segmentation in Male Pelvic CT Imagescited by 6

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