Region proposal network for lung nodule detection and segmentation

Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul Kennedy

Research output: Journal PublicationConference articlepeer-review

2 Citations (Scopus)


Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The first stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.

Original languageEnglish
Pages (from-to)48-52
Number of pages5
JournalCEUR Workshop Proceedings
Publication statusPublished - 2020
Externally publishedYes
Event5th International Workshop on Knowledge Discovery in Healthcare Data, KDH 2020 - Virtual, Santiago de Compostela, Spain
Duration: 29 Aug 202030 Aug 2020


  • Deep learning
  • Nodule segmentation
  • Region proposal network

ASJC Scopus subject areas

  • Computer Science (all)


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