Implicit Neural Representations for Medical Imaging Segmentation

Muhammad Osama Khan, Yi Fang

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


3D signals in medical imaging, such as CT scans, are usually parameterized as a discrete grid of voxels. For instance, existing state-of-the-art organ segmentation methods learn discrete segmentation maps. Unfortunately, the memory requirements of such methods grow cubically with increasing spatial resolution, which makes them unsuitable for processing high resolution scans. To overcome this, we design an Implicit Organ Segmentation Network (IOSNet) that utilizes continuous Implicit Neural Representations and has several useful properties. Firstly, the IOSNet decoder memory is roughly constant and independent of the spatial resolution since it parameterizes the segmentation map as a continuous function. Secondly, IOSNet converges much faster than discrete voxel based methods due to its ability to accurately segment organs irrespective of organ sizes, thereby alleviating size imbalance issues without requiring any auxiliary tricks. Thirdly, IOSNet naturally supports super-resolution (i.e. sampling at arbitrary resolutions during inference) due to its continuous learnt representations. Moreover, despite using a simple lightweight decoder, IOSNet consistently outperforms the discrete specialized segmentation architecture UNet. Hence, our approach demonstrates that Implicit Neural Representations are well-suited for medical imaging applications, especially for processing high-resolution 3D medical scans.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031164422
StatePublished - 2022
Event25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 18 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022


  • Implicit Neural Representations
  • Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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