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We proposed a novel U-Net-based model -- DC-UNet to do medical image segmentation.

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DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation

Result
This repository contains the implementation of a new version U-Net (DC-UNet) used to segment different types of biomedical images. This is a binary classification task: the neural network predicts if each pixel in the biomedical images is either a region of interests (ROI) or not. The neural network structure is described in this [**paper**] (https://arxiv.org/abs/2006.00414).

Architecture of DC-UNet

DC-BlockRes-path

DC-UNet

Dataset

In this project, we test three datasets:

  • Infrared Breast Dataset
  • Endoscopy (CVC-ClinicDB)
  • Electron Microscopy (ISBI-2012)

Usage

Prerequisities

The following dependencies are needed:

  • Kearas == 2.24
  • Opencv == 3.31
  • Tensorflow == 1.10.0
  • Matplotlib == 3.1.3
  • Numpy == 1.19.1

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We proposed a novel U-Net-based model -- DC-UNet to do medical image segmentation.

Topics

python3 pytorch segmentation keras-tensorflow medical-image-segmentation dc-unet dcunet

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