Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model
Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
De Zanet, Sandro I [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
automatic vitreous humor segmentation automatic optic axis detection patient-specific multimodal eye model automatic optic disc detection |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on biomedical engineering - New York, NY : IEEE, 1964, 62(2015), 2, Seite 532-540 |
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| Übergeordnetes Werk: |
volume:62 ; year:2015 ; number:2 ; pages:532-540 |
| Links: |
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| DOI / URN: |
10.1109/TBME.2014.2359676 |
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OLC1964906385 |
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| 520 | |a Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. | ||
| 650 | 4 | |a automatic fovea detection | |
| 650 | 4 | |a optical tomography | |
| 650 | 4 | |a biomedical MRI | |
| 650 | 4 | |a fundus fusion | |
| 650 | 4 | |a Retina | |
| 650 | 4 | |a Lenses | |
| 650 | 4 | |a Optical imaging | |
| 650 | 4 | |a magnetic resonance imaging | |
| 650 | 4 | |a automatic vitreous humor segmentation | |
| 650 | 4 | |a computerised tomography | |
| 650 | 4 | |a ophthalmologist | |
| 650 | 4 | |a automatic optic axis detection | |
| 650 | 4 | |a patient-specific multimodal eye model | |
| 650 | 4 | |a automatic optic disc detection | |
| 650 | 4 | |a image segmentation | |
| 650 | 4 | |a eye | |
| 650 | 4 | |a Biomedical optical imaging | |
| 650 | 4 | |a eye pathology diagnosis | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a computed tomography | |
| 650 | 4 | |a fundus photography | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a landmark detection | |
| 650 | 4 | |a optical coherence tomography | |
| 650 | 4 | |a Photography | |
| 650 | 4 | |a Optical filters | |
| 650 | 4 | |a Retinoscopy - methods | |
| 650 | 4 | |a Retinoblastoma - pathology | |
| 650 | 4 | |a Image Interpretation, Computer-Assisted - methods | |
| 650 | 4 | |a Retinal Neoplasms - pathology | |
| 650 | 4 | |a Pattern Recognition, Automated - methods | |
| 700 | 1 | |a Ciller, Carlos |4 oth | |
| 700 | 1 | |a Rudolph, Tobias |4 oth | |
| 700 | 1 | |a Maeder, Philippe |4 oth | |
| 700 | 1 | |a Munier, Francis |4 oth | |
| 700 | 1 | |a Balmer, Aubin |4 oth | |
| 700 | 1 | |a Cuadra, Meritxell Bach |4 oth | |
| 700 | 1 | |a Kowal, Jens H |4 oth | |
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10.1109/TBME.2014.2359676 doi PQ20160617 (DE-627)OLC1964906385 (DE-599)GBVOLC1964906385 (PRQ)c1545-962539931605e48d07430a9b0d8421dec30e02a842636d97893f084affe05ddd0 (KEY)0037705820150000062000200532landmarkdetectionforfusionoffundusandmritowardapat DE-627 ger DE-627 rakwb eng 620 610 DNB XA 48665 AVZ rvk 44.09 bkl 44.40 bkl De Zanet, Sandro I verfasserin aut Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. automatic fovea detection optical tomography biomedical MRI fundus fusion Retina Lenses Optical imaging magnetic resonance imaging automatic vitreous humor segmentation computerised tomography ophthalmologist automatic optic axis detection patient-specific multimodal eye model automatic optic disc detection image segmentation eye Biomedical optical imaging eye pathology diagnosis medical image processing computed tomography fundus photography MRI landmark detection optical coherence tomography Photography Optical filters Retinoscopy - methods Retinoblastoma - pathology Image Interpretation, Computer-Assisted - methods Retinal Neoplasms - pathology Pattern Recognition, Automated - methods Ciller, Carlos oth Rudolph, Tobias oth Maeder, Philippe oth Munier, Francis oth Balmer, Aubin oth Cuadra, Meritxell Bach oth Kowal, Jens H oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 62(2015), 2, Seite 532-540 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:62 year:2015 number:2 pages:532-540 http://dx.doi.org/10.1109/TBME.2014.2359676 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6906270 http://www.ncbi.nlm.nih.gov/pubmed/25265602 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 62 2015 2 532-540 |
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10.1109/TBME.2014.2359676 doi PQ20160617 (DE-627)OLC1964906385 (DE-599)GBVOLC1964906385 (PRQ)c1545-962539931605e48d07430a9b0d8421dec30e02a842636d97893f084affe05ddd0 (KEY)0037705820150000062000200532landmarkdetectionforfusionoffundusandmritowardapat DE-627 ger DE-627 rakwb eng 620 610 DNB XA 48665 AVZ rvk 44.09 bkl 44.40 bkl De Zanet, Sandro I verfasserin aut Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. automatic fovea detection optical tomography biomedical MRI fundus fusion Retina Lenses Optical imaging magnetic resonance imaging automatic vitreous humor segmentation computerised tomography ophthalmologist automatic optic axis detection patient-specific multimodal eye model automatic optic disc detection image segmentation eye Biomedical optical imaging eye pathology diagnosis medical image processing computed tomography fundus photography MRI landmark detection optical coherence tomography Photography Optical filters Retinoscopy - methods Retinoblastoma - pathology Image Interpretation, Computer-Assisted - methods Retinal Neoplasms - pathology Pattern Recognition, Automated - methods Ciller, Carlos oth Rudolph, Tobias oth Maeder, Philippe oth Munier, Francis oth Balmer, Aubin oth Cuadra, Meritxell Bach oth Kowal, Jens H oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 62(2015), 2, Seite 532-540 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:62 year:2015 number:2 pages:532-540 http://dx.doi.org/10.1109/TBME.2014.2359676 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6906270 http://www.ncbi.nlm.nih.gov/pubmed/25265602 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 62 2015 2 532-540 |
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10.1109/TBME.2014.2359676 doi PQ20160617 (DE-627)OLC1964906385 (DE-599)GBVOLC1964906385 (PRQ)c1545-962539931605e48d07430a9b0d8421dec30e02a842636d97893f084affe05ddd0 (KEY)0037705820150000062000200532landmarkdetectionforfusionoffundusandmritowardapat DE-627 ger DE-627 rakwb eng 620 610 DNB XA 48665 AVZ rvk 44.09 bkl 44.40 bkl De Zanet, Sandro I verfasserin aut Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. automatic fovea detection optical tomography biomedical MRI fundus fusion Retina Lenses Optical imaging magnetic resonance imaging automatic vitreous humor segmentation computerised tomography ophthalmologist automatic optic axis detection patient-specific multimodal eye model automatic optic disc detection image segmentation eye Biomedical optical imaging eye pathology diagnosis medical image processing computed tomography fundus photography MRI landmark detection optical coherence tomography Photography Optical filters Retinoscopy - methods Retinoblastoma - pathology Image Interpretation, Computer-Assisted - methods Retinal Neoplasms - pathology Pattern Recognition, Automated - methods Ciller, Carlos oth Rudolph, Tobias oth Maeder, Philippe oth Munier, Francis oth Balmer, Aubin oth Cuadra, Meritxell Bach oth Kowal, Jens H oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 62(2015), 2, Seite 532-540 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:62 year:2015 number:2 pages:532-540 http://dx.doi.org/10.1109/TBME.2014.2359676 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6906270 http://www.ncbi.nlm.nih.gov/pubmed/25265602 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 62 2015 2 532-540 |
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10.1109/TBME.2014.2359676 doi PQ20160617 (DE-627)OLC1964906385 (DE-599)GBVOLC1964906385 (PRQ)c1545-962539931605e48d07430a9b0d8421dec30e02a842636d97893f084affe05ddd0 (KEY)0037705820150000062000200532landmarkdetectionforfusionoffundusandmritowardapat DE-627 ger DE-627 rakwb eng 620 610 DNB XA 48665 AVZ rvk 44.09 bkl 44.40 bkl De Zanet, Sandro I verfasserin aut Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. automatic fovea detection optical tomography biomedical MRI fundus fusion Retina Lenses Optical imaging magnetic resonance imaging automatic vitreous humor segmentation computerised tomography ophthalmologist automatic optic axis detection patient-specific multimodal eye model automatic optic disc detection image segmentation eye Biomedical optical imaging eye pathology diagnosis medical image processing computed tomography fundus photography MRI landmark detection optical coherence tomography Photography Optical filters Retinoscopy - methods Retinoblastoma - pathology Image Interpretation, Computer-Assisted - methods Retinal Neoplasms - pathology Pattern Recognition, Automated - methods Ciller, Carlos oth Rudolph, Tobias oth Maeder, Philippe oth Munier, Francis oth Balmer, Aubin oth Cuadra, Meritxell Bach oth Kowal, Jens H oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 62(2015), 2, Seite 532-540 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:62 year:2015 number:2 pages:532-540 http://dx.doi.org/10.1109/TBME.2014.2359676 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6906270 http://www.ncbi.nlm.nih.gov/pubmed/25265602 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 62 2015 2 532-540 |
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10.1109/TBME.2014.2359676 doi PQ20160617 (DE-627)OLC1964906385 (DE-599)GBVOLC1964906385 (PRQ)c1545-962539931605e48d07430a9b0d8421dec30e02a842636d97893f084affe05ddd0 (KEY)0037705820150000062000200532landmarkdetectionforfusionoffundusandmritowardapat DE-627 ger DE-627 rakwb eng 620 610 DNB XA 48665 AVZ rvk 44.09 bkl 44.40 bkl De Zanet, Sandro I verfasserin aut Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. automatic fovea detection optical tomography biomedical MRI fundus fusion Retina Lenses Optical imaging magnetic resonance imaging automatic vitreous humor segmentation computerised tomography ophthalmologist automatic optic axis detection patient-specific multimodal eye model automatic optic disc detection image segmentation eye Biomedical optical imaging eye pathology diagnosis medical image processing computed tomography fundus photography MRI landmark detection optical coherence tomography Photography Optical filters Retinoscopy - methods Retinoblastoma - pathology Image Interpretation, Computer-Assisted - methods Retinal Neoplasms - pathology Pattern Recognition, Automated - methods Ciller, Carlos oth Rudolph, Tobias oth Maeder, Philippe oth Munier, Francis oth Balmer, Aubin oth Cuadra, Meritxell Bach oth Kowal, Jens H oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 62(2015), 2, Seite 532-540 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:62 year:2015 number:2 pages:532-540 http://dx.doi.org/10.1109/TBME.2014.2359676 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6906270 http://www.ncbi.nlm.nih.gov/pubmed/25265602 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 62 2015 2 532-540 |
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Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model |
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Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. |
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Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. |
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Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye. |
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Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model |
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