scholarly journals Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values

2008 ◽  
Vol 35 (12) ◽  
pp. 5375-5384 ◽  
Author(s):  
Jack H. Noble ◽  
Frank M. Warren ◽  
Robert F. Labadie ◽  
Benoit M. Dawant
Keyword(s):  
Facial Nerve ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Chorda Tympani ◽  
Feature Values

scholarly journals Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans

2011 ◽  
Vol 38 (10) ◽  
pp. 5590-5600 ◽  
Author(s):  
Fitsum A. Reda ◽  
Jack H. Noble ◽  
Alejandro Rivas ◽  
Theodore R. McRackan ◽  
Robert F. Labadie ◽  
...  
Keyword(s):  
Facial Nerve ◽  
Automatic Segmentation ◽  
Ct Scans ◽  
Chorda Tympani ◽  
Pediatric Ct

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

2020 ◽  
Author(s):  
Qi Yang ◽  
Yunke Li ◽  
Mengyi Zhang ◽  
Tian Wang ◽  
Fei Yan ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ct Images

scholarly journals Postoperative radiological assessment of the mastoid facial canal in cochlear implant patients in correlation with facial nerve stimulation

2021 ◽  
Author(s):  
Iris Burck ◽  
Rania A. Helal ◽  
Nagy N. N. Naguib ◽  
Nour-Eldin A. Nour-Eldin ◽  
Jan-Erik Scholtz ◽  
...  
Keyword(s):  
Facial Nerve ◽  
Cochlear Implant ◽  
Nerve Stimulation ◽  
Lateral Wall ◽  
Facial Canal ◽  
Chorda Tympani ◽  
Radiological Assessment ◽  
Canal Diameter ◽  
History Of ◽  
Facial Nerve Stimulation

Abstract Objectives To correlate the radiological assessment of the mastoid facial canal in postoperative cochlear implant (CI) cone-beam CT (CBCT) and other possible contributing clinical or implant-related factors with postoperative facial nerve stimulation (FNS) occurrence. Methods Two experienced radiologists evaluated retrospectively 215 postoperative post-CI CBCT examinations. The mastoid facial canal diameter, wall thickness, distance between the electrode cable and mastoid facial canal, and facial-chorda tympani angle were assessed. Additionally, the intracochlear position and the insertion angle and depth of electrodes were evaluated. Clinical data were analyzed for postoperative FNS within 1.5-year follow-up, CI type, onset, and causes for hearing loss such as otosclerosis, meningitis, and history of previous ear surgeries. Postoperative FNS was correlated with the measurements and clinical data using logistic regression. Results Within the study population (mean age: 56 ± 18 years), ten patients presented with FNS. The correlations between FNS and facial canal diameter (p = 0.09), wall thickness (p = 0.27), distance to CI cable (p = 0.44), and angle with chorda tympani (p = 0.75) were statistically non-significant. There were statistical significances for previous history of meningitis/encephalitis (p = 0.001), extracochlear-electrode-contacts (p = 0.002), scala-vestibuli position (p = 0.02), younger patients’ age (p = 0.03), lateral-wall-electrode type (p = 0.04), and early/childhood onset hearing loss (p = 0.04). Histories of meningitis/encephalitis and extracochlear-electrode-contacts were included in the first two steps of the multivariate logistic regression. Conclusion The mastoid-facial canal radiological assessment and the positional relationship with the CI electrode provide no predictor of postoperative FNS. Histories of meningitis/encephalitis and extracochlear-electrode-contacts are important risk factors. Key Points • Post-operative radiological assessment of the mastoid facial canal and the positional relationship with the CI electrode provide no predictor of post-cochlear implant facial nerve stimulation. • Radiological detection of extracochlear electrode contacts and the previous clinical history of meningitis/encephalitis are two important risk factors for postoperative facial nerve stimulation in cochlear implant patients. • The presence of scala vestibuli electrode insertion as well as the lateral wall electrode type, the younger patient’s age, and early onset of SNHL can play important role in the prediction of post-cochlear implant facial nerve stimulation.

scholarly journals Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Escudero Sanchez ◽  
Leonardo Rundo ◽  
Andrew B. Gill ◽  
Matthew Hoare ◽  
Eva Mendes Serrao ◽  
...  
Keyword(s):  
Large Fraction ◽  
Therapy Response ◽  
Response Assessment ◽  
Ct Images ◽  
Optimal Number ◽  
Image Features ◽  
Liver Tumour ◽  
Slice Thickness ◽  
Invasive Method ◽  
Feature Values

AbstractRadiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75–90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

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