Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation

Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiograph...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Carlos Cano-Espinosa [verfasserIn] Miguel Cazorla [verfasserIn] Germán González [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	pulmonary embolism computed aided detection computed tomography segmentation convolutional neural networks

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 10(2020), 8, p 2945
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:8, p 2945

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app10082945

Katalog-ID:	DOAJ055975089

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520			\|a Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification.
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allfields	10.3390/app10082945 doi (DE-627)DOAJ055975089 (DE-599)DOAJ5b4bb90ec69648f5b11a1defd931c874 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Carlos Cano-Espinosa verfasserin aut Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification. pulmonary embolism computed aided detection computed tomography segmentation convolutional neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Miguel Cazorla verfasserin aut Germán González verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 8, p 2945 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:8, p 2945 https://doi.org/10.3390/app10082945 kostenfrei https://doaj.org/article/5b4bb90ec69648f5b11a1defd931c874 kostenfrei https://www.mdpi.com/2076-3417/10/8/2945 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 8, p 2945
spelling	10.3390/app10082945 doi (DE-627)DOAJ055975089 (DE-599)DOAJ5b4bb90ec69648f5b11a1defd931c874 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Carlos Cano-Espinosa verfasserin aut Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification. pulmonary embolism computed aided detection computed tomography segmentation convolutional neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Miguel Cazorla verfasserin aut Germán González verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 8, p 2945 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:8, p 2945 https://doi.org/10.3390/app10082945 kostenfrei https://doaj.org/article/5b4bb90ec69648f5b11a1defd931c874 kostenfrei https://www.mdpi.com/2076-3417/10/8/2945 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 8, p 2945
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allfieldsSound	10.3390/app10082945 doi (DE-627)DOAJ055975089 (DE-599)DOAJ5b4bb90ec69648f5b11a1defd931c874 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Carlos Cano-Espinosa verfasserin aut Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification. pulmonary embolism computed aided detection computed tomography segmentation convolutional neural networks Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Miguel Cazorla verfasserin aut Germán González verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 8, p 2945 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:8, p 2945 https://doi.org/10.3390/app10082945 kostenfrei https://doaj.org/article/5b4bb90ec69648f5b11a1defd931c874 kostenfrei https://www.mdpi.com/2076-3417/10/8/2945 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 8, p 2945
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callnumber-first	T - Technology
author	Carlos Cano-Espinosa
spellingShingle	Carlos Cano-Espinosa misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc pulmonary embolism misc computed aided detection misc computed tomography misc segmentation misc convolutional neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation pulmonary embolism computed aided detection computed tomography segmentation convolutional neural networks
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc pulmonary embolism misc computed aided detection misc computed tomography misc segmentation misc convolutional neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc pulmonary embolism misc computed aided detection misc computed tomography misc segmentation misc convolutional neural networks misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
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title_sort	computer aided detection of pulmonary embolism using multi-slice multi-axial segmentation
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title_auth	Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
abstract	Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification.
abstractGer	Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification.
abstract_unstemmed	Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification.
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title_short	Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
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Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a <inline-formula< <math display="inline"< <semantics< <mrow< <mn<3</mn< <mi<D</mi< </mrow< </semantics< </math< </inline-formula< image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<94</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula<. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pulmonary embolism</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">computed aided detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">computed tomography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">convolutional neural networks</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). 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Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation

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