Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds

Hangfan Liu, Tanweer Rashid, Jeffrey Ware, Paul Jensen, Thomas Austin, Ilya Nasrallah, Robert Bryan, Susan Heckbert, Mohamad Habes

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

2 Scopus citations

Abstract

Deep learning for medical image analysis requires large quantities of high-quality imaging data for training purposes, which could be often less available due to existence of heavy noise in particular imaging modalities. This issue is especially obvious in cerebral microbleed (CMB) detection, since CMBs are more discernable on long echo time (TE) susceptibility weighted imaging (SWI) data, which are unfortunately much noisier than those with shorter TE. In this paper we present an effective unsupervised image denoising scheme with application to boosting the performance of deep learning based CMB detection. The proposed content-adaptive denoising technique uses the log-determinant of covariance matrices formed by highly correlated image contents retrieved from the input itself to implicitly and efficiently exploit sparsity in PCA domain. The numerical solution to the corresponding optimization problem comes down to an adaptive squeeze-and-shrink (ASAS) operation on the underlying PCA coefficients. Obviously, the ASAS denoising does not rely on any external dataset and could be better fit the input image data. Experiments on medical image datasets with synthetic Gaussian white noise demonstrate that the proposed ASAS scheme is highly competitive among state-of-the-art sparsity based approaches as well as deep learning based method. When applied to the deep learning based CMB detection on the real-world TE3 SWI dataset, the proposed ASAS denoising could improve the precision by 18.03%, sensitivity by 7.64%, and increase the correlation between counts of ground truth and automated detection by 19.87%.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Pages265-275
Number of pages11
ISBN (Print)9783030872304
DOIs
StatePublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: Sep 27 2021Oct 1 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12906 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period9/27/2110/1/21

Keywords

  • Cerebral microbleed detection
  • Deep learning
  • Image denoising
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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