Validation of active shape model techniques for intracochlear anatomy segmentation in computed tomography images.

Purpose: Cochlear implants (CIs) have been shown to be highly effective restorative devices for patients suffering from severe-to-profound hearing loss. Hearing outcomes with CIs depend on electrode positions with respect to intracochlear anatomy. Intracochlear anatomy can only be directly visualized using high-resolution modalities, such as micro-computed tomography (µCT), which cannot be used in vivo. However, active shape models (ASM) have been shown to be robust and effective for segmenting intracochlear anatomy in large scale datasets of patient computed tomographies (CTs). We present an extended dataset of µCT specimens and aim to evaluate the ASM's performance more comprehensively than has been previously possible. Approach: Using a dataset of 16 manually segmented cochlea specimens on µCTs, we found parameters that optimize mean CT segmentation performance and then evaluate the effect of library size on the ASM. The optimized ASM was further evaluated on a clinical dataset of 134 CT images to assess method reliability. Results: Optimized parameters lead to mean CT segmentation performance to 0.36 mm point-to-point error, 0.10 mm surface error, and 0.83 Dice score. Larger library sizes provide diminishing returns on segmentation performance and total variance captured by the ASM. We found our method to be clinically reliable with the main performance limitation that was found to be the candidate search process rather than model representation. Conclusions: We have presented a comprehensive validation of the ASM for use in intracochlear anatomy segmentation. These results are critical to understand the limitations of the method for clinical use and for future development.

Automatic Segmentation of Intracochlear Anatomy in MR Images Using a Weighted Active Shape Model.

There is evidence that cochlear MR signal intensity may be useful in prognosticating the risk of hearing loss after middle cranial fossa (MCF) resection of acoustic neuroma (AN), but the manual segmentation of this structure is difficult and prone to error. This hampers both large-scale retrospective studies and routine clinical use of this information. To address this issue, we present a fully automatic method that permits the segmentation of the intra-cochlear anatomy in MR images, which uses a weighted active shape model we have developed and validated to segment the intra-cochlear anatomy in CT images. We take advantage of a dataset for which both CT and MR images are available to validate our method on 132 ears in 66 high-resolution T2-weighted MR images. Using the CT segmentation as ground truth, we achieve a mean Dice (DSC) value of 0.81 and 0.79 for the scala tympani (ST) and the scala vestibuli (SV), which are the two main intracochlear structures.Clinical Relevance- The proposed method is accurate and fully automated for MR image segmentation. It can be used to support large retrospective studies that explore relations between MR signal in preoperative images and outcomes. It can also facilitate the routine and clinical use of this information.

Insertion Depth for Optimized Positioning of Precurved Cochlear Implant Electrodes.

HYPOTHESIS: Generic guidelines for insertion depth of precurved electrodes are suboptimal for many individuals. BACKGROUND: Insertion depths that are too shallow result in decreased cochlear coverage, and ones that are too deep lift electrodes away from the modiolus and degrade the electro-neural interface. Guidelines for insertion depth are generically applied to all individuals using insertion depth markers on the array that can be referenced against anatomical landmarks. METHODS: To normalize our measurements, we determined the optimal position and insertion vector where a precurved array best fits the cochlea for each patient in an IRB-approved, N = 131 subject CT database. The distances from the most basal electrode on an optimally placed array to anatomical landmarks, including the round window (RW) and facial recess (FR), was measured for all patients. RESULTS: The standard deviations of the distance from the most basal electrode to the FR and RW are 0.65 mm and 0.26 mm, respectively. Owing to the high variability in FR distance, using the FR as a landmark to determine insertion depth results in >0.5 mm difference with ideal depth in 44% of cases. Alignment of either of the two most proximal RW markers with the RW would result in over-insertion failures for >80% of cases, whereas the use of the third, most medial marker would result in under-insertion in only 19% of cases. CONCLUSIONS: Normalized measurements using the optimized insertion vector show low variance in distance from the basal electrode position to the RW, thereby suggesting it as a better landmark for determining insertion depth than the FR.