DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation

Nikhil Kumar Tomar, Debesh Jha*, Ulas Bagci

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time speed are the essential obstacles to be overcome for successful clinical integration of such systems. While literature is focused on improving accuracy, the speed parameter is often ignored. Toward this critical need, we intend to develop a novel real-time deep learning-based architecture, DilatedSegNet, to perform polyp segmentation on the fly. DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps. Each of these feature maps is passed through a dilated convolution pooling (DCP) block. The outputs from the DCP blocks are concatenated and passed through a series of four decoder blocks that predicts the segmentation mask. The proposed method achieves a real-time operation speed of 33.68 frames per second with an average dice coefficient (DSC) of 0.90 and mIoU of 0.83. Additionally, we also provide heatmap along with the qualitative results that shows the explanation for the polyp location, which increases the trustworthiness of the method. The results on the publicly available Kvasir-SEG and BKAI-IGH datasets suggest that DilatedSegNet can give real-time feedback while retaining a high DSC, indicating high potential for using such models in real clinical settings in the near future. The GitHub link of the source code can be found here: https://github.com/nikhilroxtomar/DilatedSegNet.

Original languageEnglish (US)
Title of host publicationMultiMedia Modeling - 29th International Conference, MMM 2023, Proceedings
EditorsDuc-Tien Dang-Nguyen, Cathal Gurrin, Alan F. Smeaton, Martha Larson, Stevan Rudinac, Minh-Son Dao, Christoph Trattner, Phoebe Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031270765
StatePublished - 2023
Event29th International Conference on MultiMedia Modeling, MMM 2023 - Bergen, Norway
Duration: Jan 9 2023Jan 12 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13833 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on MultiMedia Modeling, MMM 2023


  • Colonoscopy
  • Deep learning
  • Generalization
  • Polyp segmentation
  • Real-time segmentation
  • Residual network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation'. Together they form a unique fingerprint.

Cite this