PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans

Nikan, Soodeh; Osch, Kylen Van; Bartling, Mandolin Li; Allen, Daniel; Rohani, Sohrab; Connors, Ben; Agrawal, Sumit; Ladak, Hanif M.

doi:10.1109/tip.2020.3038363

Cited by 38 publications

(66 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An angle-based frequency function was additionally determined for the SG, however this was generalized with respect to individual cochlear angular length as SG variability was not observed to be related to any clinically obtainable measurement. Prospectively, these angle-based frequency mapping techniques can be combined with automated image processing algorithms [53] capable of determining the angular length of the BM and post-operative cochlear implant electrodes. The angular length of the BM and electrode angular depths can currently be manually measured using clinical CT data, however this is a time intensive process, and the automatic measurement of these values would allow for custom pitch mapping without additional clinical workload.…”

Section: Discussionmentioning

confidence: 99%

“…The angular length of the BM and electrode angular depths can currently be manually measured using clinical CT data, however this is a time intensive process, and the automatic measurement of these values would allow for custom pitch mapping without additional clinical workload. Deep learning based cochlear segmentation tools have been developed in our lab [53], and future work includes the extension of this work for automatic cochlear segmentation and measurement in clinical CT data. This can be feasibly completed with a large dataset, and this work is currently underway as the next step of this project.…”

Section: Discussionmentioning

confidence: 99%

“…To reduce the requirement for manual measurements, future work of this project will involve automating the input measurements required for these functions through automatic cochlear segmentation [53]. In addition to the dependency on individual angular length, the functions were fit using an approach designed to minimize the semitone or perceptual error.…”

Section: E Angle-based Frequency Mapping Functionsmentioning

confidence: 99%

See 2 more Smart Citations

An Approach for Individualized Cochlear Frequency Mapping Determined From 3D Synchrotron Radiation Phase-Contrast Imaging

Helpard

Rohani

et al. 2021

IEEE Trans. Biomed. Eng.

Self Cite

View full text Add to dashboard Cite

Objective: Cochlear implants are traditionally programmed to stimulate according to a generalized frequency map, where individual anatomic variability is not considered when selecting the centre frequency of stimulation of each implant electrode. However, high variability in cochlear size and spatial frequency distributions exist among individuals. Generalized cochlear implant frequency maps can result in large pitch perception errors and reduced hearing outcomes for cochlear implant recipients. The objective of this work was to develop an individualized frequency mapping technique for the human cochlea to allow for patient-specific cochlear implant stimulation. Methods: Ten cadaveric human cochleae were scanned using synchrotron radiation phase-contrast imaging (SR-PCI) combined with computed tomography (CT). For each cochlea, ground truth angle-frequency measurements were obtained in three-dimensions using the SR-PCI CT data. Using an approach designed to minimize perceptual error in frequency estimation, an individualized frequency function was determined to relate angular depth to frequency within the cochlea. Results: The individualized frequency mapping function significantly reduced pitch errors in comparison to the current gold standard generalized approach. Conclusion and Significance: This paper presents for the first time a cochlear frequency map which can be individualized using only the angular length of cochleae. This approach can be applied in the clinical setting and has the potential to revolutionize cochlear implant programming for patients worldwide.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: E Angle-based Frequency Mapping Functionsmentioning

confidence: 99%

See 1 more Smart Citation

An Approach for Individualized Cochlear Frequency Mapping Determined From 3D Synchrotron Radiation Phase-Contrast Imaging

Helpard

Rohani

et al. 2021

IEEE Trans. Biomed. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several recent studies used deep learning techniques to segment inner ears and related organs in medical images. Some studies were conducted on computed tomography images to segment other organs, such as the sigmoid sinus, facial nerves, or temporal bones [ 25 , 26 ]. Another study analyzed magnetic resonance images for labyrinth segmentation [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study

Park¹,

Cho²,

Chung³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Recently, the analysis of endolymphatic hydropses (EHs) via inner ear magnetic resonance imaging (MRI) for patients with Ménière disease has been attempted in various studies. In addition, artificial intelligence has rapidly been incorporated into the medical field. In our previous studies, an automated algorithm for EH analysis was developed by using a convolutional neural network. However, several limitations existed, and further studies were conducted to compensate for these limitations. Objective The aim of this study is to develop a fully automated analytic system for measuring EH ratios that enhances EH analysis accuracy and clinical usability when studying Ménière disease via MRI. Methods We proposed the 3into3Inception and 3intoUNet networks. Their network architectures were based on those of the Inception-v3 and U-Net networks, respectively. The developed networks were trained for inner ear segmentation by using the magnetic resonance images of 124 people and were embedded in a new, automated EH analysis system—inner-ear hydrops estimation via artificial intelligence (INHEARIT)-version 2 (INHEARIT-v2). After fivefold cross-validation, an additional test was performed by using 60 new, unseen magnetic resonance images to evaluate the performance of our system. The INHEARIT-v2 system has a new function that automatically selects representative images from a full MRI stack. Results The average segmentation performance of the fivefold cross-validation was measured via the intersection of union method, resulting in performance values of 0.743 (SD 0.030) for the 3into3Inception network and 0.811 (SD 0.032) for the 3intoUNet network. The representative magnetic resonance slices (ie, from a data set of unseen magnetic resonance images) that were automatically selected by the INHEARIT-v2 system only differed from a maximum of 2 expert-selected slices. After comparing the ratios calculated by experienced physicians and those calculated by the INHEARIT-v2 system, we found that the average intraclass correlation coefficient for all cases was 0.941; the average intraclass correlation coefficient of the vestibules was 0.968, and that of the cochleae was 0.914. The time required for the fully automated system to accurately analyze EH ratios based on a patient's MRI stack was approximately 3.5 seconds. Conclusions In this study, a fully automated full-stack magnetic resonance analysis system for measuring EH ratios was developed (named INHEARIT-v2), and the results showed that there was a high correlation between the expert-calculated EH ratio values and those calculated by the INHEARIT-v2 system. The system is an upgraded version of the INHEARIT system; it has higher segmentation performance and automatically selects representative images from an MRI stack. The new model can help clinicians by providing objective analysis results and reducing the workload for interpreting magnetic resonance images.

show abstract

“…This paper proposes a deep learning-based bony orbital segmentation method, which can achieve automatic segmentation of bony orbit and can greatly reduce the influence of human subjective errors and be more efficient. There have been some segmentation networks such as UNet [ 22 ] and FCN [ 12 ], which have been widely used in human tissues such as brain tissue [ 16 ], lung [ 5 ], blood vessels [ 1 ], and temporal bone [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

et al. 2022

View full text Add to dashboard Cite

Purpose Current research on the aging of bony orbit is usually done manually, which is inefficient and has a large error. In this paper, automatic segmentation of bony orbit based on deep learning and automatic calculation of the parameters of the segmented orbital contour (area and height of bony orbit) are presented. Methods The craniofacial CT scanning data of 595 Chinese were used to carry out three-dimensional reconstruction and output the craniofacial images. The orbital contour images are obtained automatically by UNet++ segmentation network, and then the bony orbital area and height were calculated automatically by connected component analysis. Results The automatic segmentation method has an Intersection of Union of 95.41% in craniofacial CT images. During the aging, the bony orbital area of males increased with age, while that of females decreased, and the area in male was larger than that in female ( P < 0.05). The distance from equal points 10 and 40–90 to the supraorbital rim was significantly larger ( P < 0.05). Except for the equal point 90, the distance from equal points to the inferior orbital rim was obviously larger ( P < 0.05). In the females, the distance from equal points 50–70 to inferior orbital rim was significantly lower ( P < 0.05). Conclusion The method proposed here can automatically and accurately study image dataset of large-scale bony orbital CT imaging. UNet++ can achieve high-precision segmentation of bony orbital contours. The bony orbital area of Chinese changes with aging, and the bony orbital height changes different between males and females, which may be caused by the different position and degree of orbital bone resorption of males and females in the process of aging.

show abstract

PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans

Cited by 38 publications

References 43 publications

An Approach for Individualized Cochlear Frequency Mapping Determined From 3D Synchrotron Radiation Phase-Contrast Imaging

An Approach for Individualized Cochlear Frequency Mapping Determined From 3D Synchrotron Radiation Phase-Contrast Imaging

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study

Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

Contact Info

Product

Resources

About