Robert F. Labadie

Ange Lou, Yamin Li, Yike Zhang et al.

The Segment Anything Model 2 (SAM 2) is the latest generation foundation model for image and video segmentation. Trained on the expansive Segment Anything Video (SA-V) dataset, which comprises 35.5 million masks across 50.9K videos, SAM 2 advances its predecessor's capabilities by supporting zero-shot segmentation through various prompts (e.g., points, boxes, and masks). Its robust zero-shot performance and efficient memory usage make SAM 2 particularly appealing for surgical tool segmentation in videos, especially given the scarcity of labeled data and the diversity of surgical procedures. In this study, we evaluate the zero-shot video segmentation performance of the SAM 2 model across different types of surgeries, including endoscopy and microscopy. We also assess its performance on videos featuring single and multiple tools of varying lengths to demonstrate SAM 2's applicability and effectiveness in the surgical domain. We found that: 1) SAM 2 demonstrates a strong capability for segmenting various surgical videos; 2) When new tools enter the scene, additional prompts are necessary to maintain segmentation accuracy; and 3) Specific challenges inherent to surgical videos can impact the robustness of SAM 2.

Yiyuan Zhao, Jianing Wang, Rui Li et al.

Cochlear implants (CIs) are a standard treatment for patients who experience severe to profound hearing loss. Recent studies have shown that hearing outcome is correlated with intra-cochlear anatomy and electrode placement. Our group has developed image-guided CI programming (IGCIP) techniques that use image analysis methods to both segment the inner ear structures in pre- or post-implantation CT images and localize the CI electrodes in post-implantation CT images. This permits to assist audiologists with CI programming by suggesting which among the contacts should be deactivated to reduce electrode interaction that is known to affect outcomes. Clinical studies have shown that IGCIP can improve hearing outcomes for CI recipients. However, the sensitivity of IGCIP with respect to the accuracy of the two major steps: electrode localization and intra-cochlear anatomy segmentation, is unknown. In this article, we create a ground truth dataset with conventional CT and micro-CT images of 35 temporal bone specimens to both rigorously characterize the accuracy of these two steps and assess how inaccuracies in these steps affect the overall results. Our study results show that when clinical pre- and post-implantation CTs are available, IGCIP produces results that are comparable to those obtained with the corresponding ground truth in 86.7% of the subjects tested. When only post-implantation CTs are available, this number is 83.3%. These results suggest that our current method is robust to errors in segmentation and localization but also that it can be improved upon. Keywords: cochlear implant, ground truth, segmentation, validation