Image segmentation-based volume approximation—volume as a factor in the clinical management of osteolytic jaw lesions

Kauke, Martin; Safi, Ali-Farid; Grandoch, Andrea; Nickenig, Hans-Joachim; Zöller, Joachim E.; Kreppel, Matthias

doi:10.1259/dmfr.20180113

Cited by 13 publications

(25 citation statements)

References 26 publications

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“…So, in clinical applications of segmentation, especially prior to surgical operations in complicated cases, the reliability of the manual segmentation shall be questioned. Image segmentation-based volume assessment is presented as an accurate method that provides comparative analysis of jaw lesions (17). However, the results of the present study showed that mean radiographic volume measurements for manual, semi-automatic and automatic segmentation were significantly lower than the gold standard values.…”

Section: Discussioncontrasting

confidence: 67%

“…and neurogenic pathologies associated with the mandibular nerve (17,18). Segmentation is also exploited for establishment of morphological changes within the temporomandibular joint to provide data about functional/pathological alterations of the mandibular complex (19,20).…”

Section: Introductionmentioning

confidence: 99%

“…Segmentation is also exploited for establishment of morphological changes within the temporomandibular joint to provide data about functional/pathological alterations of the mandibular complex (19,20). Additionally, for detection of the volume of neoplastic or nonneoplastic osteolytic lesions of the jaws, automatic segmentation on CBCT images can provide promising results (17,21). In orthodontics, development of the external root resorption can be observed by calculation of the volume of the tooth (4).…”

Section: Introductionmentioning

confidence: 99%

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Is Manual Segmentation the Real Gold Standard for Tooth Segmentation? A Preliminary <i>in vivo</i> Study Using Cone-beam Computed Tomography Images

Sabanci¹,

Şener²,

Turhal³

et al. 2021

meandros

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Objective: This study aimed to assess whether manual segmentation is an accurate method in tooth volume measurement and to compare the outcomes of manual, automatic, and semiautomatic segmentations on cone-beam computed tomography (CBCT) images by comparing each system with the water displacement method, which is the gold standard. Materials and Methods: CBCT images of l0 maxillary impacted teeth were used in this preliminary in vivo study. Following the acquisition of CBCT scans, manual, automatic, and semiautomatic segmentations were completed by the same operator. After surgical removal, the volumes of all impacted teeth were measured with the water displacement method, which was used as the gold standard. The volume of each segmented image was measured in mm 3 using the 3D-Doctor software. The established volumes of each segmented image were compared with those of the gold standard using the 95% confidence interval bootstrap percentiles. Intraobserver reliability was determined using the intraclass correlation coefficient. Results: All segmentation methods revealed significantly different volume values both from the gold standard and from each other (p=0.000). The semiautomatic segmentation demonstrated comparable performance with the manual method, and both systems provided comparable volumes with the gold standard than did the automatic method. Excellent intra-observer intraclass correlations were found for all protocols. Conclusion:The actual volumes of the specimen were not obtained by manual, semiautomatic, and automatic segmentations. Semiautomatic segmentation demonstrated comparable performance to the manual method, whereas automatic segmentation yielded the poorest values. The automatic and semiautomatic segmentations may be improved by the development and utilization of novel or hybrid segmentation algorithms for a faster process and more accurate results.

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Section: Discussioncontrasting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Is Manual Segmentation the Real Gold Standard for Tooth Segmentation? A Preliminary <i>in vivo</i> Study Using Cone-beam Computed Tomography Images

Sabanci¹,

Şener²,

Turhal³

et al. 2021

meandros

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“…The program was initially validated by analyzing MRI images of the caudate nucleus of the brain. Multiple consecutive volumetric studies confirmed these results 13,15,17–19 …”

Section: Methodsmentioning

confidence: 67%

A Volumetric and Morphological Analysis of Recurrent Odontogenic Keratocysts by Semiautomatic Segmentation

Lentzen

Riekert

Zirk

et al. 2021

Journal of Craniofacial Surgery

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The authors conducted this study to provide morphological and volumetric data of recurrent odontogenic keratocysts of the upper and lower jaw to emphasize risk factors in accordance with their radiological appearance and guide clinical decisions for jeopardized patients. Methods: By applying the open-source software ''ITK-Snap'' on cone-beam computed tomography images, volumetric measurements of histopathologically diagnosed recurrent odontogenic keratocysts could be performed. For statistical investigations, descriptive statistics and independent Student t test were performed. The intraclass correlation coefficient was used to assess intra-and inter-rater reliabilities. P values P < 0.05 were considered significant. Results: Forty patients (24 male and 16 female) were included in this study. Recurrent odontogenic keratocysts had a mean maximum diameter of 28.91 mm AE 12.00 mm and a mean volume of 4.48 cm 3 AE 4.29 cm 3 . According to morphology, irregular shape (P ¼ 0.001; P ¼ 0.005), unclear margin (P ¼ 0.001; P ¼ 0.001), multilocular morphology (P ¼ 0.001; P ¼ 0.001), and cortical bone exceedance (P ¼ 0.001; P ¼ 0.007) are statistically significantly associated with a larger cyst diameter and volume. Furthermore, significant differences by diameter and volume could be shown between patients with and without iliac crest graft reconstruction (P ¼ 0.001; P ¼ 0.001). Conclusions: Volumetric analysis reveals that recurrent odontogenic keratocysts show large diametric and volumetric extension that leads to complex reconstruction by iliac crest grafts, adding an argument that special attention should be paid to this entity and its recurrence. in case of difficult histopathological examination, lesions with irregular shape and margin, multilocular morphology, cortical bone exceedance, and clinically visible symptoms should be considered for close morphological and volumetric clinicoradiological follow-up.

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“…ITK-SNAP also provides automated segmentation using the level-set method [72] which allows segmentation of structures that appear homogeneous in medical images using little human interaction. This tool has been widely applied in many several areas such as cranio-facial pathologies and anatomical studies [73], carotid artery segmentation [74], diffusion MRI analysis [75], prenatal image analysis [76] and virtual reality in Medicine [77], among others. A screenshot of ITK-SNAP is shown in Fig.…”

Section: Image Annotationsmentioning

confidence: 99%

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

et al. 2021

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The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field.

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Image segmentation-based volume approximation—volume as a factor in the clinical management of osteolytic jaw lesions

Cited by 13 publications

References 26 publications

Is Manual Segmentation the Real Gold Standard for Tooth Segmentation? A Preliminary <i>in vivo</i> Study Using Cone-beam Computed Tomography Images

Is Manual Segmentation the Real Gold Standard for Tooth Segmentation? A Preliminary <i>in vivo</i> Study Using Cone-beam Computed Tomography Images

A Volumetric and Morphological Analysis of Recurrent Odontogenic Keratocysts by Semiautomatic Segmentation

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

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