Relaxed Statistical Shape Models for 3D Image Segmentation – Application to Mandible Bone in Cone-beam CT Data

Gollmer, Sebastian T.; Buzug, Thorsten M.

doi:10.2174/1573405611309020008

Cited by 2 publications

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“…Moreover, several publications on SSM-based segmentation methods have been used in combination with other strategies to enhance the segmentation outcomes. Hybrid methods have demonstrated good results [ 18 , 37 , 38 , 39 ]. Kainmueller et al [ 39 ] used SSM adaptation for mandible segmentation and then utilized the graph cut algorithm for fine segmentation in the MICCAI 2019 challenge [ 112 ].…”

Section: Resultsmentioning

confidence: 99%

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Automatic Segmentation of Mandible from Conventional Methods to Deep Learning—A Review

Qiu

Wel

Kraeima

et al. 2021

JPM

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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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Section: Resultsmentioning

confidence: 99%

“…The authors established correspondence by optimizing a model-based cost function. Moreover, the authors introduced a relaxed SSM method for mandible segmentation [ 38 ]. Abdolali et al [ 18 ] proposed a framework based on SSM and fast marching for automatic segmentation of the mandibular bone and canal.…”

Section: Resultsmentioning

confidence: 99%

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning—A Review

Qiu

Wel

Kraeima

et al. 2021

JPM

View full text Add to dashboard Cite

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Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms

Lin

2021

2021 IEEE 23rd Int Conf on High Performance Computing &Amp; Communications; 7th Int Conf on Data Science &Amp; Systems; 19th In

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Relaxed Statistical Shape Models for 3D Image Segmentation – Application to Mandible Bone in Cone-beam CT Data

Cited by 2 publications

References 0 publications

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning—A Review

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning—A Review

Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms

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