Detail publikace

White Blood Cell Segmentation Using Fully Convolutional Neural Networks

HESKO, B. HARABIŠ, V. KRÁLÍK, M.

Originální název

White Blood Cell Segmentation Using Fully Convolutional Neural Networks

Anglický název

White Blood Cell Segmentation Using Fully Convolutional Neural Networks

Jazyk

en

Originální abstrakt

In medicine, the identification and counting of white blood cells are used for diagnosing diseases like inflammation, malignancy or leukaemia. In this paper, we propose a novel approach to white blood cell segmentation. On two different white blood cell datasets, two networks, PSPNet and U-Net are trained to perform simultaneous nucleus and cytoplasm segmentation. Compared to ground truth, our segmentations are almost identical, with smoother borders. When comparing overall cell segmentation with current methods, our networks are achieving similar (or better) results in evaluated metrics, with intersection over union reaching around 0.95 for both networks. DICE coefficient is higher than 0.96 for both networks and both datasets, which is a promising result of the segmentation.

Anglický abstrakt

In medicine, the identification and counting of white blood cells are used for diagnosing diseases like inflammation, malignancy or leukaemia. In this paper, we propose a novel approach to white blood cell segmentation. On two different white blood cell datasets, two networks, PSPNet and U-Net are trained to perform simultaneous nucleus and cytoplasm segmentation. Compared to ground truth, our segmentations are almost identical, with smoother borders. When comparing overall cell segmentation with current methods, our networks are achieving similar (or better) results in evaluated metrics, with intersection over union reaching around 0.95 for both networks. DICE coefficient is higher than 0.96 for both networks and both datasets, which is a promising result of the segmentation.

Dokumenty

BibTex


@article{BUT150871,
  author="Branislav {Hesko} and Vratislav {Harabiš} and Martin {Králík}",
  title="White Blood Cell Segmentation Using Fully Convolutional Neural Networks",
  annote="In medicine, the identification and counting of white blood cells are used for diagnosing diseases like inflammation, malignancy or leukaemia. In this paper, we propose a novel approach to white blood cell segmentation. On two different white blood cell datasets, two networks, PSPNet and U-Net are trained to perform simultaneous nucleus and cytoplasm segmentation. Compared to ground truth, our segmentations are almost identical, with smoother borders. When comparing overall cell segmentation with current methods, our networks are achieving similar (or better) results in evaluated metrics, with intersection over union reaching around 0.95 for both networks. DICE coefficient is higher than 0.96 for both networks and both datasets, which is a promising result of the segmentation.",
  chapter="150871",
  howpublished="online",
  number="5",
  volume="20",
  year="2018",
  month="october",
  pages="1--9",
  type="journal article - other"
}