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Qiu B, van der Wel H, Kraeima J, Glas HH, Guo J, Borra RJH, Witjes MJH, van Ooijen PMA. Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review. J Pers Med 2021; 11:629. [PMID: 34357096 PMCID: PMC8307673 DOI: 10.3390/jpm11070629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 01/05/2023] Open
Abstract
Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications.
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Affiliation(s)
- Bingjiang Qiu
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Hylke van der Wel
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Joep Kraeima
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Haye Hendrik Glas
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Jiapan Guo
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Ronald J. H. Borra
- Medical Imaging Center (MIC), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Max Johannes Hendrikus Witjes
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Peter M. A. van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Rigaud B, Simon A, Castelli J, Gobeli M, Ospina Arango JD, Cazoulat G, Henry O, Haigron P, De Crevoisier R. Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy. BIOMED RESEARCH INTERNATIONAL 2015; 2015:726268. [PMID: 25759821 PMCID: PMC4339705 DOI: 10.1155/2015/726268] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Revised: 01/16/2015] [Accepted: 01/16/2015] [Indexed: 11/18/2022]
Abstract
In the context of head and neck cancer (HNC) adaptive radiation therapy (ART), the two purposes of the study were to compare the performance of multiple deformable image registration (DIR) methods and to quantify their impact for dose accumulation, in healthy structures. Fifteen HNC patients had a planning computed tomography (CT0) and weekly CTs during the 7 weeks of intensity-modulated radiation therapy (IMRT). Ten DIR approaches using different registration methods (demons or B-spline free form deformation (FFD)), preprocessing, and similarity metrics were tested. Two observers identified 14 landmarks (LM) on each CT-scan to compute LM registration error. The cumulated doses estimated by each method were compared. The two most effective DIR methods were the demons and the FFD, with both the mutual information (MI) metric and the filtered CTs. The corresponding LM registration accuracy (precision) was 2.44 mm (1.30 mm) and 2.54 mm (1.33 mm), respectively. The corresponding LM estimated cumulated dose accuracy (dose precision) was 0.85 Gy (0.93 Gy) and 0.88 Gy (0.95 Gy), respectively. The mean uncertainty (difference between maximal and minimal dose considering all the 10 methods) to estimate the cumulated mean dose to the parotid gland (PG) was 4.03 Gy (SD = 2.27 Gy, range: 1.06-8.91 Gy).
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Affiliation(s)
- Bastien Rigaud
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Antoine Simon
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Joël Castelli
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
| | - Maxime Gobeli
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
| | - Juan-David Ospina Arango
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Guillaume Cazoulat
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Olivier Henry
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
| | - Pascal Haigron
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
| | - Renaud De Crevoisier
- Université de Rennes 1, LTSI, Campus de Beaulieu, 35000 Rennes, France
- INSERM, U1099, Campus de Beaulieu, 35000 Rennes, France
- Centre Eugene Marquis, Radiotherapy Department, 35000 Rennes, France
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