3D Medical Image Lossless Compressor Using Deep Learning Approaches

Nagoor, Omniah

doi:10.23889/SUthesis.61753

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3D Medical Image Lossless Compressor Using Deep Learning Approaches / Omniah Nagoor

Swansea University Author: Omniah Nagoor

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DOI (Published version): 10.23889/SUthesis.61753

Abstract

The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in eﬃcient han...

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Published:	Swansea 2022
Institution:	Swansea University
Degree level:	Doctoral
Degree name:	Ph.D
Supervisor:	Jones, Mark W.
URI:	https://cronfa.swan.ac.uk/Record/cronfa61753
Tags:	Add Tag my 1 phd 1 thesis 1

first_indexed	2022-11-01T15:01:35Z
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Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies eﬀectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an eﬀective compression method. Since medical information is critical and imposes an inﬂuential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable eﬀorts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information eﬃciently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the diﬀerences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the eﬀectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input conﬁgurations and sampling schemes for a many-to-one sequence prediction model, speciﬁcally for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by diﬀerent hospitals, representing diﬀerent body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are signiﬁcantly more informative than others, speciﬁcally in medical domains where samples are available on a scale of billions. 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spelling	2022-11-01T15:58:11.1958474 v2 61753 2022-11-01 3D Medical Image Lossless Compressor Using Deep Learning Approaches ad8a0ed9b747350e0d626fe4398a9fe0 Omniah Nagoor Omniah Nagoor true false 2022-11-01 SCS The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in eﬃcient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies eﬀectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an eﬀective compression method. Since medical information is critical and imposes an inﬂuential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable eﬀorts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information eﬃciently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the diﬀerences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the eﬀectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input conﬁgurations and sampling schemes for a many-to-one sequence prediction model, speciﬁcally for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by diﬀerent hospitals, representing diﬀerent body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are signiﬁcantly more informative than others, speciﬁcally in medical domains where samples are available on a scale of billions. The eﬀectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling. E-Thesis Swansea Lossless Compression, Deep learning, 3D Predictor, Medical Image Compression, Sequence prediction model, LSTM, Volumetric Data Compression, Neural Network, CT, MRI 28 10 2022 2022-10-28 10.23889/SUthesis.61753 ORCiD identifier: https://orcid.org/0000-0002-6329-1293 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Jones, Mark W. Doctoral Ph.D 2022-11-01T15:58:11.1958474 2022-11-01T14:57:06.4669608 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Omniah Nagoor 1 61753__25633__075e79f24f014a93a92c6561a5a051b7.pdf Nagoor_Omniah_PhD_Thesis_Final_Redacted_Signature.pdf 2022-11-01T15:20:33.7049863 Output 82578163 application/pdf E-Thesis – open access true Copyright: The author, Omniah H. J. Nagoor, 2022. true eng
title	3D Medical Image Lossless Compressor Using Deep Learning Approaches
spellingShingle	3D Medical Image Lossless Compressor Using Deep Learning Approaches Omniah Nagoor
title_short	3D Medical Image Lossless Compressor Using Deep Learning Approaches
title_full	3D Medical Image Lossless Compressor Using Deep Learning Approaches
title_fullStr	3D Medical Image Lossless Compressor Using Deep Learning Approaches
title_full_unstemmed	3D Medical Image Lossless Compressor Using Deep Learning Approaches
title_sort	3D Medical Image Lossless Compressor Using Deep Learning Approaches
author_id_str_mv	ad8a0ed9b747350e0d626fe4398a9fe0
author_id_fullname_str_mv	ad8a0ed9b747350e0d626fe4398a9fe0_***_Omniah Nagoor
author	Omniah Nagoor
author2	Omniah Nagoor
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institution	Swansea University
doi_str_mv	10.23889/SUthesis.61753
college_str	Faculty of Science and Engineering
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description	The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in eﬃcient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies eﬀectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an eﬀective compression method. Since medical information is critical and imposes an inﬂuential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable eﬀorts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information eﬃciently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the diﬀerences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the eﬀectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input conﬁgurations and sampling schemes for a many-to-one sequence prediction model, speciﬁcally for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by diﬀerent hospitals, representing diﬀerent body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are signiﬁcantly more informative than others, speciﬁcally in medical domains where samples are available on a scale of billions. The eﬀectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling.
published_date	2022-10-28T04:20:49Z
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score	11.013731

3D Medical Image Lossless Compressor Using Deep Learning Approaches / Omniah Nagoor

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