BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Teng Li [verfasserIn] Yanzhe Xu [verfasserIn] Teresa Wu [verfasserIn] Jennifer R. Charlton [verfasserIn] Kevin M. Bennett [verfasserIn] Firas Al-Hindawi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	imaging biomarker blob detection glomeruli segmentation Hessian analysis contrastive learning

Übergeordnetes Werk:	In: Bioengineering - MDPI AG, 2014, 10(2023), 12, p 1372
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:12, p 1372

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/bioengineering10121372

Katalog-ID:	DOAJ098910523

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520			\|a Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.
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allfields	10.3390/bioengineering10121372 doi (DE-627)DOAJ098910523 (DE-599)DOAJ0e5af824e5cf45eb8697affd266ec1e4 DE-627 ger DE-627 rakwb eng QH301-705.5 Teng Li verfasserin aut BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency. imaging biomarker blob detection glomeruli segmentation Hessian analysis contrastive learning Technology T Biology (General) Yanzhe Xu verfasserin aut Teresa Wu verfasserin aut Jennifer R. Charlton verfasserin aut Kevin M. Bennett verfasserin aut Firas Al-Hindawi verfasserin aut In Bioengineering MDPI AG, 2014 10(2023), 12, p 1372 (DE-627)774814020 (DE-600)2746191-9 23065354 nnns volume:10 year:2023 number:12, p 1372 https://doi.org/10.3390/bioengineering10121372 kostenfrei https://doaj.org/article/0e5af824e5cf45eb8697affd266ec1e4 kostenfrei https://www.mdpi.com/2306-5354/10/12/1372 kostenfrei https://doaj.org/toc/2306-5354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 12, p 1372
spelling	10.3390/bioengineering10121372 doi (DE-627)DOAJ098910523 (DE-599)DOAJ0e5af824e5cf45eb8697affd266ec1e4 DE-627 ger DE-627 rakwb eng QH301-705.5 Teng Li verfasserin aut BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency. imaging biomarker blob detection glomeruli segmentation Hessian analysis contrastive learning Technology T Biology (General) Yanzhe Xu verfasserin aut Teresa Wu verfasserin aut Jennifer R. Charlton verfasserin aut Kevin M. Bennett verfasserin aut Firas Al-Hindawi verfasserin aut In Bioengineering MDPI AG, 2014 10(2023), 12, p 1372 (DE-627)774814020 (DE-600)2746191-9 23065354 nnns volume:10 year:2023 number:12, p 1372 https://doi.org/10.3390/bioengineering10121372 kostenfrei https://doaj.org/article/0e5af824e5cf45eb8697affd266ec1e4 kostenfrei https://www.mdpi.com/2306-5354/10/12/1372 kostenfrei https://doaj.org/toc/2306-5354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 12, p 1372
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authorswithroles_txt_mv	Teng Li @@aut@@ Yanzhe Xu @@aut@@ Teresa Wu @@aut@@ Jennifer R. Charlton @@aut@@ Kevin M. Bennett @@aut@@ Firas Al-Hindawi @@aut@@
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callnumber-first	Q - Science
author	Teng Li
spellingShingle	Teng Li misc QH301-705.5 misc imaging biomarker misc blob detection misc glomeruli segmentation misc Hessian analysis misc contrastive learning misc Technology misc T misc Biology (General) BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging
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topic_title	QH301-705.5 BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging imaging biomarker blob detection glomeruli segmentation Hessian analysis contrastive learning
topic	misc QH301-705.5 misc imaging biomarker misc blob detection misc glomeruli segmentation misc Hessian analysis misc contrastive learning misc Technology misc T misc Biology (General)
topic_unstemmed	misc QH301-705.5 misc imaging biomarker misc blob detection misc glomeruli segmentation misc Hessian analysis misc contrastive learning misc Technology misc T misc Biology (General)
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title	BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging
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title_sort	blobcut: a contrastive learning method to support small blob detection in medical imaging
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title_auth	BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging
abstract	Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.
abstractGer	Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.
abstract_unstemmed	Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.
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title_short	BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging
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author2	Yanzhe Xu Teresa Wu Jennifer R. Charlton Kevin M. Bennett Firas Al-Hindawi
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up_date	2024-07-03T19:56:13.368Z
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BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging

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