Automatic cochlear multimodal 3D image segmentation and analysis using atlas-model-based method.

Objectives: To propose an automated fast cochlear segmentation, length, and volume estimation method from clinical 3D multimodal images which has a potential role in the choice of cochlear implant type, surgery planning, and robotic surgeries.

Methods: Two datasets from different countries were used. These datasets include 219 clinical 3D images of cochlea from 3 modalities: CT, CBCT, and MR. The datasets include different ages, genders, and types of cochlear implants. We propose an atlas-model-based method for cochlear segmentation and measurement based on high-resolution μCT model and A-value. The method was evaluated using 3D landmarks located by two experts.

Results: The average error was $0.61\pm 0.22$ mm and the average time required to process an image was $5.21\pm 0.93$ seconds (P<0.001). The volume of the cochlea ranged from 73.96 mm${}^3$ to 106.97 mm${}^3$, the cochlear length ranged from 36.69 to 45.91 mm at the lateral wall and from 29.12 to 39.05 mm at the organ of Corti.

Discussion: We propose a method that produces nine different automated measurements of the cochlea: volume of scala tympani, volume of scala vestibuli, central lengths of the two scalae, the scala tympani lateral wall length, and the organ of Corti length in addition to three measurements related to A-value.

Conclusion: This automatic cochlear image segmentation and analysis method can help clinician process multimodal cochlear images in approximately 5 seconds using a simple computer. The proposed method is publicly available for free download as an extension for 3D Slicer software.