Isbi 2017


The conference paper Nuclei Segmentation in Histopathology Images Using Deep Neural Networks by Peter Naylor, Marick Laé, Fabien Reyal and Thomas Walter has been published in Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on (pp. 933-936).

Data publicaly available

The data used for the study can be found here here. The files come as a zip file and has many subdirectories. Patients histopathological data can be found under “Slide_id” and the ground truth can be found under “GT_id” where id is the patients id. Histopathology data are under a standard RGB files, however the ground truth is under an itk-snap format which is nii.gz. One could use the nibabel to open such images.

Summary of the paper

We note 3 contributions:

We used ITK-snap for the annotation.

Figure 1: Random samples from the dataset

Table 1: Metric comparaison of methods

Figure 2: Visual comparaison of methods

FCN: refers to the method in the paper: Long, Jonathan, Evan Shelhamer, and Trevor Darrell. “Fully convolutional networks for semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

PangNet: refers to the method in the paper: Pang Baochuan, Zhang Yi, Chen Qianqing, Gao Zhifan, Peng Qinmu and You Xinge, “Cell nucleus segmentation in color histopathological imagery using convolutional networks,” in Pattern Recognition (CCPR), 2010 Chinese Conference on. IEEE, 2010, pp. 1–5.

DeconvNet: refers to the method in the paper: Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, “Learning deconvolution network for semantic segmentation,” arXiv preprint arXiv :1505.04366, 2015.

ITK-snap: refers to the software developed in the paper: Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig, “User-guided 3D active contour segmentation of anatomical structures : Significantly improved efficiency and reliability,” Neuroimage, vol. 31, no. 3, pp. 1116–1128, 2006.