HEp-2 Specimen Image Segmentation and Classification Using Very Deep Fully Convolutional Network

Yuexiang Li, Linlin Shen, Shiqi Yu

Research output: Journal PublicationArticlepeer-review

50 Citations (Scopus)


Reliable identification of Human Epithelial-2 (HEp-2) cell patterns can facilitate the diagnosis of systemic autoimmune diseases. However, traditional approach requires experienced experts to manually recognize the cell patterns, which suffers from the inter-observer variability. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to simultaneously address the segmentation and classification problem of HEp-2 specimen images. The proposed system transforms the residual network (ResNet) to fully convolutional ResNet (FCRN) enabling the network to perform semantic segmentation task. A sand-clock shape residual module is proposed to effectively and economically improve the performance of FCRN. The publicly available I3A-2014 data set was used to train the FCRN model to classify HEp-2 specimen images into seven catalogs: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. The proposed system achieves a mean class accuracy of 94.94% for leave-one-out tests, which outperforms the winner of ICPR 2014, i.e., 89.93%. At the same time, our model also achieves a segmentation accuracy of 89.03%, which is 19.05% higher than that of the benchmark approach, i.e., 69.98%.

Original languageEnglish
Article number7862234
Pages (from-to)1561-1572
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number7
Publication statusPublished - Jul 2017
Externally publishedYes


  • Cell patterns
  • classification
  • fully convolutional network
  • segmentation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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