Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns
Dynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) is a valid complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the amount of data, the examination is difficult without the support of a computer‐aided detection and diagnosis (CAD) system....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-10-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5273 |
| _version_ | 1827817495706206208 |
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| author | Gabriele Piantadosi Stefano Marrone Roberta Fusco Mario Sansone Carlo Sansone |
| author_facet | Gabriele Piantadosi Stefano Marrone Roberta Fusco Mario Sansone Carlo Sansone |
| author_sort | Gabriele Piantadosi |
| collection | DOAJ |
| description | Dynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) is a valid complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the amount of data, the examination is difficult without the support of a computer‐aided detection and diagnosis (CAD) system. Since magnetic resonance imaging data includes different tissues and patient movements (i.e. breathing) may introduce artefacts during acquisition, CADs need some stages aimed to identify breast parenchyma and to reduce motion artefacts. Among the major issues in developing a fully automated CAD, there are the accurate segmentation of lesions in regions of interest and their consequent staging (classification). This work introduces breast lesion automatic detection and diagnosis system (BLADeS), a comprehensive fully automated breast CAD aimed to support the radiologist during the patient diagnosis. The authors propose a hierarchical architecture that implements modules for breast segmentation, attenuation of motion artefacts, localisation of lesions and, finally, classification according to their malignancy. Performance was evaluated on 42 patients with histopathologically proven lesions, performing cross‐validation to ensure a fair comparison. Results show that BLADeS can be successfully used to perform a fully automated breast lesion diagnosis starting from T1‐weighted DCE‐MRI, without requiring any operator interaction in any of the processing stages. |
| first_indexed | 2024-03-12T00:35:00Z |
| format | Article |
| id | doaj.art-49c0ef022c6448bea923ae02300e4380 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:35:00Z |
| publishDate | 2018-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-49c0ef022c6448bea923ae02300e43802023-09-15T09:52:03ZengWileyIET Computer Vision1751-96321751-96402018-10-011271007101710.1049/iet-cvi.2018.5273Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patternsGabriele Piantadosi0Stefano Marrone1Roberta Fusco2Mario Sansone3Carlo Sansone4DIETI – University of Naples Federico IIVia Claudio 2180125NapoliItalyDIETI – University of Naples Federico IIVia Claudio 2180125NapoliItalyDepartment of Diagnostic ImagingNational Cancer Institute of Naples ‘Pascale Foundation’Via M. Semmola 5380131NapoliItalyDIETI – University of Naples Federico IIVia Claudio 2180125NapoliItalyDIETI – University of Naples Federico IIVia Claudio 2180125NapoliItalyDynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) is a valid complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the amount of data, the examination is difficult without the support of a computer‐aided detection and diagnosis (CAD) system. Since magnetic resonance imaging data includes different tissues and patient movements (i.e. breathing) may introduce artefacts during acquisition, CADs need some stages aimed to identify breast parenchyma and to reduce motion artefacts. Among the major issues in developing a fully automated CAD, there are the accurate segmentation of lesions in regions of interest and their consequent staging (classification). This work introduces breast lesion automatic detection and diagnosis system (BLADeS), a comprehensive fully automated breast CAD aimed to support the radiologist during the patient diagnosis. The authors propose a hierarchical architecture that implements modules for breast segmentation, attenuation of motion artefacts, localisation of lesions and, finally, classification according to their malignancy. Performance was evaluated on 42 patients with histopathologically proven lesions, performing cross‐validation to ensure a fair comparison. Results show that BLADeS can be successfully used to perform a fully automated breast lesion diagnosis starting from T1‐weighted DCE‐MRI, without requiring any operator interaction in any of the processing stages.https://doi.org/10.1049/iet-cvi.2018.5273comprehensive computer-aided diagnosisbreast T1-weighted DCE-MRIquantitative dynamical featuresdynamic contrast enhanced-magnetic resonance imagingearly detectionbreast cancer |
| spellingShingle | Gabriele Piantadosi Stefano Marrone Roberta Fusco Mario Sansone Carlo Sansone Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns IET Computer Vision comprehensive computer-aided diagnosis breast T1-weighted DCE-MRI quantitative dynamical features dynamic contrast enhanced-magnetic resonance imaging early detection breast cancer |
| title | Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns |
| title_full | Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns |
| title_fullStr | Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns |
| title_full_unstemmed | Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns |
| title_short | Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns |
| title_sort | comprehensive computer aided diagnosis for breast t1 weighted dce mri through quantitative dynamical features and spatio temporal local binary patterns |
| topic | comprehensive computer-aided diagnosis breast T1-weighted DCE-MRI quantitative dynamical features dynamic contrast enhanced-magnetic resonance imaging early detection breast cancer |
| url | https://doi.org/10.1049/iet-cvi.2018.5273 |
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