MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive...

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Autores principales:	Parvez Ahmad, Hai Jin, Roobaea Alroobaea, Saqib Qamar, Ran Zheng, Fady Alnajjar, Fathia Aboudi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	BraTS convolutions dense connections encoder-decoder ISLES MICCAI Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0c79ab71e3b744aea7085540d141fd80
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spelling	oai:doaj.org-article:0c79ab71e3b744aea7085540d141fd802021-11-18T00:11:11ZMH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation2169-353610.1109/ACCESS.2021.3122543https://doaj.org/article/0c79ab71e3b744aea7085540d141fd802021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585109/https://doaj.org/toc/2169-3536UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 <italic>Magnetic Resonance Imaging</italic> (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 <italic>Computed Tomographic Perfusion</italic> (CTP) testing dataset. MH UNet is available at <uri>https://github.com/parvezamu/MHUnet</uri>.Parvez AhmadHai JinRoobaea AlroobaeaSaqib QamarRan ZhengFady AlnajjarFathia AboudiIEEEarticleBraTSconvolutionsdense connectionsencoder-decoderISLESMICCAIElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148384-148408 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	BraTS convolutions dense connections encoder-decoder ISLES MICCAI Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	BraTS convolutions dense connections encoder-decoder ISLES MICCAI Electrical engineering. Electronics. Nuclear engineering TK1-9971 Parvez Ahmad Hai Jin Roobaea Alroobaea Saqib Qamar Ran Zheng Fady Alnajjar Fathia Aboudi MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
description	UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 <italic>Magnetic Resonance Imaging</italic> (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 <italic>Computed Tomographic Perfusion</italic> (CTP) testing dataset. MH UNet is available at <uri>https://github.com/parvezamu/MHUnet</uri>.
format	article
author	Parvez Ahmad Hai Jin Roobaea Alroobaea Saqib Qamar Ran Zheng Fady Alnajjar Fathia Aboudi
author_facet	Parvez Ahmad Hai Jin Roobaea Alroobaea Saqib Qamar Ran Zheng Fady Alnajjar Fathia Aboudi
author_sort	Parvez Ahmad
title	MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
title_short	MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
title_full	MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
title_fullStr	MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
title_full_unstemmed	MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
title_sort	mh unet: a multi-scale hierarchical based architecture for medical image segmentation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0c79ab71e3b744aea7085540d141fd80
work_keys_str_mv	AT parvezahmad mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation AT haijin mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation AT roobaeaalroobaea mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation AT saqibqamar mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation AT ranzheng mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation AT fadyalnajjar mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation AT fathiaaboudi mhunetamultiscalehierarchicalbasedarchitectureformedicalimagesegmentation
_version_	1718425144044552192

MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

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