Combining CNN with Pathological Information for the Detection of Transmissive Lesions of Jawbones from CBCT Images
Cone-Beam Computed Tomography (CBCT) imaging modality is used to acquire 3D volumetric image of the human body. CBCT plays a vital role in diagnosing dental diseases, especially cyst or tumour-like lesions. Current computer-aided detection and diagnostic systems have demonstrated diagnostic value in a range of diseases, however, the capability of such a deep learning method on transmissive lesions has not been investigated. In this study, we propose an automatic method for the detection of transmissive lesions of jawbones using CBCT images. We integrated a pre-trained DenseNet with pathological information to reduce the intra-class variation within a patient's images in the 3D volume (stack) that may affect the performance of the model. Our proposed method separates each CBCT stacks into seven intervals based on their disease manifestation. To evaluate the performance of our method, we created a new dataset containing 353 patients' CBCT data. A patient-wise image division strategy was employed to split the training and test sets. The overall lesion detection accuracy of 80.49% was achieved, outperforming the baseline DenseNet result of 77.18%. The result demonstrates the feasibility of our method for detecting transmissive lesions in CBCT images.Clinical relevance - The proposed strategy aims at providing automatic detection of the transmissive lesions of jawbones with the use of CBCT images that can reduce the workload of clinical radiologists, improve their diagnostic efficiency, and meet the preliminary requirement for the diagnosis of this kind of disease when there is a lack of radiologists.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2021 |
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| Enthalten in: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2021(2021) vom: 17. Nov., Seite 2972-2975 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huang, Zimo [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 30.12.2021 Date Revised 30.12.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.1109/EMBC46164.2021.9630692 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM334283973 |
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| 520 | |a Cone-Beam Computed Tomography (CBCT) imaging modality is used to acquire 3D volumetric image of the human body. CBCT plays a vital role in diagnosing dental diseases, especially cyst or tumour-like lesions. Current computer-aided detection and diagnostic systems have demonstrated diagnostic value in a range of diseases, however, the capability of such a deep learning method on transmissive lesions has not been investigated. In this study, we propose an automatic method for the detection of transmissive lesions of jawbones using CBCT images. We integrated a pre-trained DenseNet with pathological information to reduce the intra-class variation within a patient's images in the 3D volume (stack) that may affect the performance of the model. Our proposed method separates each CBCT stacks into seven intervals based on their disease manifestation. To evaluate the performance of our method, we created a new dataset containing 353 patients' CBCT data. A patient-wise image division strategy was employed to split the training and test sets. The overall lesion detection accuracy of 80.49% was achieved, outperforming the baseline DenseNet result of 77.18%. The result demonstrates the feasibility of our method for detecting transmissive lesions in CBCT images.Clinical relevance - The proposed strategy aims at providing automatic detection of the transmissive lesions of jawbones with the use of CBCT images that can reduce the workload of clinical radiologists, improve their diagnostic efficiency, and meet the preliminary requirement for the diagnosis of this kind of disease when there is a lack of radiologists | ||
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| 700 | 1 | |a Zhang, Lefei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bo |e verfasserin |4 aut | |
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