BAWGNet: Boundary aware wavelet guided network for the nuclei segmentation in histopathology images

Tamjid Imtiaz, Shaikh Anowarul Fattah, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Precise cell nucleus segmentation is very critical in many biologically related analyses and disease diagnoses. However, the variability in nuclei structure, color, and modalities of histopathology images make the automatic computer-aided nuclei segmentation task very difficult. Traditional encoder–decoder based deep learning schemes mainly utilize the spatial domain information that may limit the performance of recognizing small nuclei regions in subsequent downsampling operations. In this paper, a boundary aware wavelet guided network (BAWGNet) is proposed by incorporating a boundary aware unit along with an attention mechanism based on a wavelet domain guidance in each stage of the encoder–decoder output. Here the high-frequency 2 Dimensional discrete wavelet transform (2D-DWT) coefficients are utilized in the attention mechanism to guide the spatial information obtained from the encoder–decoder output stages to leverage the nuclei segmentation task. On the other hand, the boundary aware unit (BAU) captures the nuclei's boundary information, ensuring accurate prediction of the nuclei pixels in the edge region. Furthermore, the preprocessing steps used in our methodology confirm the data's uniformity by converting it to similar color statistics. Extensive experimentations conducted on three benchmark histopathology datasets (DSB, MoNuSeg and TNBC) exhibit the outstanding segmentation performance of the proposed method (with dice scores 90.82%, 85.74%, and 78.57%, respectively). Implementation of the proposed architecture is available at https://github.com/tamjidimtiaz/BAWGNet.

Original languageEnglish (US)
Article number107378
JournalComputers in Biology and Medicine
Volume165
DOIs
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications

Keywords

  • Attention mechanism
  • Boundary aware unit
  • Deep neural network
  • Discrete wavelet transform
  • Nucleus segmentation

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