Study of morphologic variability of incudostapedial angle and its relation with temporal bone pneumatization

Singh, Anup; Irugu, David Victor Kumar; Kumar, Rajeev; Verma, Hitesh; Gupta, Anandita; Kumari, Abha

doi:10.1016/j.joto.2019.09.004

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Importantly, these results also help to objectively describe the regions of greatest variation within these landmarks, which is both surgically relevant and helpful in studying how anatomical variations affect auditory and vestibular function. While several other groups have published SSMs of temporal bone anatomy, the majority of have focused on single structures (3,4,15,17,19,20), and none to the best of our knowledge have described the variability of multiple structures at a scale seen in this study. For example, understanding the second genu has the greatest variation along the course of the facial nerve can help to inform surgeons of where it is important to proceed with particular caution in a translabyrinthine approach or total facial nerve decompression.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical shape models (SSMs) are one such powerful analytical method that can mathematically define the geometrical properties (i.e., average shape and structural variation) of a set of 3D objects (13), for instance a set of facial nerves. SSMs are a valuable tool in helping to define and analyze human anatomy because they can comprehensively capture the total shape of an object, rather than using predefined, fixed measurements of lengths and angles as done in traditional manual investigations of the temporal bone (14)(15)(16)(17). Furthermore, SSMs allow researchers to objectively define regions, or modes, of variation in anatomical structures, through a method called principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

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Statistical Shape Model of the Temporal Bone Using Segmentation Propagation

Ding¹,

Lu²,

et al. 2022

Otology &Amp; Neurotology

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HypothesisAutomated image registration techniques can successfully determine anatomical variation in human temporal bones with statistical shape modeling.BackgroundThere is a lack of knowledge about inter-patient anatomical variation in the temporal bone. Statistical shape models (SSMs) provide a powerful method for quantifying variation of anatomical structures in medical images but are time-intensive to manually develop. This study presents SSMs of temporal bone anatomy using automated image-registration techniques.MethodsFifty-three cone-beam temporal bone CTs were included for SSM generation. The malleus, incus, stapes, bony labyrinth, and facial nerve were automatically segmented using 3D Slicer and a template-based segmentation propagation technique. Segmentations were then used to construct SSMs using MATLAB. The first three principal components of each SSM were analyzed to describe shape variation.ResultsPrincipal component analysis of middle and inner ear structures revealed novel modes of anatomical variation. The first three principal components for the malleus represented variability in manubrium length (mean: 4.47 mm; ±2-SDs: 4.03–5.03 mm) and rotation about its long axis (±2-SDs: -1.6° to 1.8° posteriorly). The facial nerve exhibits variability in first and second genu angles. The bony labyrinth varies in the angle between the posterior and superior canals (mean: 88.9°; ±2-SDs: 83.7°–95.7°) and cochlear orientation (±2-SDs: -4.0° to 3.0° anterolaterally).ConclusionsSSMs of temporal bone anatomy can inform surgeons on clinically relevant inter-patient variability. Anatomical variation elucidated by these models can provide novel insight into function and pathophysiology. These models also allow further investigation of anatomical variation based on age, BMI, sex, and geographical location.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Shape Model of the Temporal Bone Using Segmentation Propagation

Ding¹,

Lu²,

et al. 2022

Otology &Amp; Neurotology

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“…Prior methods for calculating clinically relevant anatomical measurements typically involve manual segmentation of structures on CT imaging with placement of fiducials on visualization software [6][7][8] or meticulous harvesting of cadaveric structures with direct manual measurements. [9][10][11] By manually segmenting a single average bone template along with clinical relevant landmarks, this image registration-based method is able to automatically propagate template labels to other temporal bone scans with reliable accuracy. We believe the ability to automate this process will have applications in current preoperative planning workflows and large-scale studies of temporal bone anatomy, as well as for future work in integrating image-guided robotics into neurotologic surgery.…”

Section: Discussionmentioning

confidence: 99%

“…This is the first study, to our knowledge, to validate a method for automated anatomical measurement extraction with reliable accuracy. Prior methods for calculating clinically relevant anatomical measurements typically involve manual segmentation of structures on CT imaging with placement of fiducials on visualization software 6‐8 or meticulous harvesting of cadaveric structures with direct manual measurements 9‐11 . By manually segmenting a single average bone template along with clinical relevant landmarks, this image registration‐based method is able to automatically propagate template labels to other temporal bone scans with reliable accuracy.…”

Section: Discussionmentioning

confidence: 99%

Automated Extraction of Anatomical Measurements From Temporal Bone CT Imaging

Ding¹,

Lu²,

et al. 2022

Otolaryngol.--head neck surg.

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Objective Proposed methods of minimally invasive and robot-assisted procedures within the temporal bone require measurements of surgically relevant distances and angles, which often require time-consuming manual segmentation of preoperative imaging. This study aims to describe an automatic segmentation and measurement extraction pipeline of temporal bone cone-beam computed tomography (CT) scans. Study Design Descriptive study of temporal bone measurements. Setting Academic institution. Methods A propagation template composed of 16 temporal bone CT scans was formed with relevant anatomical structures and landmarks manually segmented. Next, 52 temporal bone CT scans were autonomously segmented using deformable registration techniques from the Advanced Normalization Tools Python package. Anatomical measurements were extracted via in-house Python algorithms. Extracted measurements were compared to ground truth values from manual segmentations. Results Paired t test analyses showed no statistical difference between measurements using this pipeline and ground truth measurements from manually segmented images. Mean (SD) malleus manubrium length was 4.39 (0.34) mm. Mean (SD) incus short and long processes were 2.91 (0.18) mm and 3.53 (0.38) mm, respectively. The mean (SD) maximal diameter of the incus long process was 0.74 (0.17) mm. The first and second facial nerve genus had mean (SD) angles of 68.6 (6.7) degrees and 111.1 (5.3) degrees, respectively. The facial recess had a mean (SD) span of 3.21 (0.46) mm. Mean (SD) minimum distance between the external auditory canal and tegmen was 3.79 (1.05) mm. Conclusions This is the first study to automatically extract relevant temporal bone anatomical measurements from CT scans using segmentation propagation. Measurements from these models can streamline preoperative planning, improve future segmentation techniques, and help develop future image-guided or robot-assisted systems for temporal bone procedures.

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