Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network

Medical diagnosis can be severely hindered by distorted medical images, especially in the analysis of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images. Therefore, enhancing the accuracy of diagnostic imaging and inpainting damaged areas are essential for medical diagnosis. Over t...

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Published in:	Sadhana (Bangalore) Vol. 49; no. 2; p. 181
Main Authors:	Kumar, Puranam Revanth, Shilpa, B, Jha, Rajesh Kumar, Raju, B Deevena, Mohammed, Thayyaba Khatoon
Format:	Journal Article
Language:	English
Published:	New Delhi Springer India 18.05.2024 Springer Nature B.V
Subjects:	Algorithms Computed tomography Computer vision Deep learning Design Diagnosis Engineering Human error Image quality Image reconstruction Magnetic resonance imaging Medical imaging Neural networks Optimization techniques Parameter robustness Pixels Signal to noise ratio Tomography Deep learning Medical image inpainting Object removal Image translation Medical image analysis
ISSN:	0973-7677, 0256-2499, 0973-7677
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Abstract	Medical diagnosis can be severely hindered by distorted medical images, especially in the analysis of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images. Therefore, enhancing the accuracy of diagnostic imaging and inpainting damaged areas are essential for medical diagnosis. Over the past decade, image inpainting techniques have advanced due to deep learning and multimedia information. In this paper, we proposed a deep convolutional autoencoder network with improved parameters as a robust method for inpainting non-anatomical objects in MRI and CT images. Traditional approaches based on the exemplar methods are much less effective than deep learning methods in capturing high-level features. However, the inpainted regions would appear blurr and with global inconsistency. To handle the fuzzy problem, we enhanced the network model by introducing skip connections between mirrored layers in the encoder and decoder stacks. This allowed the generative process of the inpainting region to directly use the low-level feature information of the processed image. To provide both pixel-accurate and local-global contents consistency, the proposed model is trained with a combination of the typical pixel-wise reconstruction loss and two adversarial losses, which makes the inpainted output seem more realistic and consistent with its surrounding contexts. As a result, the proposed approach is much faster than existing methods while providing unprecedented qualitative and quantitative evaluation with a high inpainting inception score of 10.58, peak signal-to-noise ratio (PSNR) 52.44, structural similarity index (SSIM) 0.95, universal image quality index (UQI) 0.96, and mean squared error (MSE) 40.73 for CT and MRI images. This offers a promising avenue for enhancing image fidelity, potentially advancing clinical decision-making and patient care in neuroimaging practice.
AbstractList	Medical diagnosis can be severely hindered by distorted medical images, especially in the analysis of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images. Therefore, enhancing the accuracy of diagnostic imaging and inpainting damaged areas are essential for medical diagnosis. Over the past decade, image inpainting techniques have advanced due to deep learning and multimedia information. In this paper, we proposed a deep convolutional autoencoder network with improved parameters as a robust method for inpainting non-anatomical objects in MRI and CT images. Traditional approaches based on the exemplar methods are much less effective than deep learning methods in capturing high-level features. However, the inpainted regions would appear blurr and with global inconsistency. To handle the fuzzy problem, we enhanced the network model by introducing skip connections between mirrored layers in the encoder and decoder stacks. This allowed the generative process of the inpainting region to directly use the low-level feature information of the processed image. To provide both pixel-accurate and local-global contents consistency, the proposed model is trained with a combination of the typical pixel-wise reconstruction loss and two adversarial losses, which makes the inpainted output seem more realistic and consistent with its surrounding contexts. As a result, the proposed approach is much faster than existing methods while providing unprecedented qualitative and quantitative evaluation with a high inpainting inception score of 10.58, peak signal-to-noise ratio (PSNR) 52.44, structural similarity index (SSIM) 0.95, universal image quality index (UQI) 0.96, and mean squared error (MSE) 40.73 for CT and MRI images. This offers a promising avenue for enhancing image fidelity, potentially advancing clinical decision-making and patient care in neuroimaging practice.
ArticleNumber	181
Author	Jha, Rajesh Kumar Raju, B Deevena Mohammed, Thayyaba Khatoon Kumar, Puranam Revanth Shilpa, B
Author_xml	– sequence: 1 givenname: Puranam Revanth orcidid: 0000-0002-9141-9901 surname: Kumar fullname: Kumar, Puranam Revanth email: revanth123451.rk@gmail.com organization: Department of Artificial Intelligence and Machine Learning, School of Engineering, Malla Reddy University – sequence: 2 givenname: B surname: Shilpa fullname: Shilpa, B organization: Department of Computer Science and Engineering, AVN Institute of Engineering and Technology – sequence: 3 givenname: Rajesh Kumar surname: Jha fullname: Jha, Rajesh Kumar email: rajeshjha@ifheindia.org organization: Department of Electronics and Communication Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education – sequence: 4 givenname: B Deevena surname: Raju fullname: Raju, B Deevena organization: Department of DS & AI, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education – sequence: 5 givenname: Thayyaba Khatoon surname: Mohammed fullname: Mohammed, Thayyaba Khatoon organization: Department of Artificial Intelligence and Machine Learning, School of Engineering, Malla Reddy University
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SubjectTerms	Algorithms Computed tomography Computer vision Deep learning Design Diagnosis Engineering Human error Image quality Image reconstruction Magnetic resonance imaging Medical imaging Neural networks Optimization techniques Parameter robustness Pixels Signal to noise ratio Tomography
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Title	Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network
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