A cochlear implant (CI) includes an electrode array (EA) that is inserted into the cochlea to restore hearing. Localizing the EA in postoperative computed tomography (CT) images is needed in image-guided CI programming, which has been shown to improve hearing outcomes. Postoperative images with adequate image quality are required to allow the EA to be reliably and precisely localized. However, these images are sometimes affected by motion artifacts, which can make the localization task unreliable or even cause it to fail. Thus, flagging these low-quality images prior to the subsequent clinical use is important. In this work, we propose to assess the image quality by using a 3D convolutional neural network to classify the level (no/mild/moderate/severe) of the motion artifacts that affect the image. To address the challenges of subjective annotations and class imbalance, several techniques (a new loss term, an oversampling strategy, and motion artifact simulation) are used during training. Results show that our proposed method can achieve accuracy values of 81% in the four-class motion artifact classification and 88% in binary classification (no vs. some artifacts), demonstrating that the proposed method has the potential to reduce time and effort in image quality assessment that is traditionally done through visual inspection.
Cochlear implants (CI) are a highly successful neural-prosthetic device, recreating the sensation of hearing by directly stimulating the nerve fibers inside the cochlea for individuals experiencing severe to profound hearing loss. Implantation traditionally requires invasive procedures such as mastoidectomy, however minimally invasive techniques such as percutaneous cochlear access have also been investigated. This method involves drilling a single hole through the skull surface, granting direct access to the cochlea where the CI can be threaded. The trajectory of this insertion typically involves traversing the facial recess, a region approximately 1.0–3.5 mm in width bounded posteriorly by the facial nerve and anteriorly by the chorda tympani The determination of a safe drilling trajectory is highly crucial, as damage to these structures during surgery may result in a loss of taste (chorda) or facial paralysis (facial nerve). It is therefore very important that these clinical structures are segmented accurately for the drilling trajectory planning process. In this work, we propose the use of a conditional generative adversarial network (cGAN) to automatically segment the facial nerve. Our method can also make up for noisy and disconnected generated segmentations using a minimum cost path search function between the endpoints. Our network utilized weakly supervised approach, being trained on a small sample of 12 manually segmented image and supplemented with 120 automatically segmented image created through atlas-based image registration. Our method generated segmentations with an average mean surface error of only 0.24mm, reducing the mean error of the original method by ~50%.
Cochlear implant (CI) surgery requires manual or robotic insertion of an electrode array into the patient’s cochlea. At the vast majority of institutions including ours, preoperative CT scans are acquired and used to plan the procedure because they permit to visualize the bony anatomy of the temporal bone. However, CT images involve ionizing radiation, and some institutions and surgeons prefer preoperative MRI, especially for children. To expand the number of patients who can benefit from a computer-assisted CT-based planning system we are developing without additional radiation exposure, we propose to use a conditional generative adversarial network (cGAN)-based method to generate synthetic CT (sCT) images from multi-sequence MR images. We use image quality-based, segmentation-based, and planning-based metrics to compare the sCTs with the corresponding real CTs (rCTs). Loss terms were used to improve the quality of the overall image and of the local regions containing critical structures used for planning. We found very good agreement between the segmentations of structures in the sCTs and the corresponding rCTs with Dice values equal to 0.94 for the labyrinth, 0.79 for the ossicles, and 0.81 for the facial nerve. Such a high Dice value for the ossicles is noteworthy because they cannot be seen in the MR images. Furthermore, we found that the mean errors for quantities used for preoperative insertion plans were smaller than what is humanly perceivable. Our results strongly suggest that potential CI recipients who only have MR scans can benefit from CT-based preoperative planning through sCT generation.
Purpose: Robust and accurate segmentation methods for the intracochlear anatomy (ICA) are a critical step in the image-guided cochlear implant programming process. We have proposed an active shape model (ASM)-based method and a deep learning (DL)-based method for this task, and we have observed that the DL method tends to be more accurate than the ASM method while the ASM method tends to be more robust.
Approach: We propose a DL-based U-Net-like architecture that incorporates ASM segmentation into the network. A quantitative analysis is performed on a dataset that consists of 11 cochlea specimens for which a segmentation ground truth is available. To qualitatively evaluate the robustness of the method, an experienced expert is asked to visually inspect and grade the segmentation results on a clinical dataset made of 138 image volumes acquired with conventional CT scanners and of 39 image volumes acquired with cone beam CT (CBCT) scanners. Finally, we compare training the network (1) first with the ASM results, and then fine-tuning it with the ground truth segmentation and (2) directly with the specimens with ground truth segmentation.
Results: Quantitative and qualitative results show that the proposed method increases substantially the robustness of the DL method while having only a minor detrimental effect (though not significant) on its accuracy. Expert evaluation of the clinical dataset shows that by incorporating the ASM segmentation into the DL network, the proportion of good segmentation cases increases from 60/177 to 119/177 when training only with the specimens and increases from 129/177 to 151/177 when pretraining with the ASM results.
Conclusions: A hybrid ASM and DL-based segmentation method is proposed to segment the ICA in CT and CBCT images. Our results show that combining DL and ASM methods leads to a solution that is both robust and accurate.
Cochlear implants (CIs) are neuroprosthetic devices that can improve hearing in patients with severe-to-profound hearing loss. Postoperatively, a CI device needs to be programmed by an audiologist to determine parameter settings that lead to the best outcomes. Our group has developed an image-guided cochlear implant programming (IGCIP) system to simplify this laborious post-programming procedure and improve hearing outcomes. IGCIP requires image processing techniques to analyze the location of the inserted electrode arrays (EAs) with respect to the intracochlear anatomy (ICA). An active shape model (ASM)-based method is currently in routine use in our IGCIP system for ICA segmentation. Recently, we have proposed a hybrid ASM/deep learning (DL) segmentation method that improves segmentation accuracy. In this work, we first evaluate the effect of this method on so-called distance-vs.-frequency curves (DVFs), which permit to visualize electrode interaction and are used to provide programming guidance. An expert evaluation study is then performed to manually configure the electrodes based on the DVFs and grade the quality of the electrode configurations derived from ASM and hybrid ASM/DL segmentations compared to the one derived from ground truth segmentation. Results we have obtained show that the hybrid ASM/DL segmentation technique tends to generate DVFs with smaller frequency error and distance error, and electrode configurations which are comparable to the existing ASM-based method.
Cochlear implants (CIs) are neuroprosthetic devices that can improve hearing in patients with severe-to-profound hearing loss. Postoperatively, a CI device needs to be programmed by an audiologist to determine parameter settings that lead to the best outcomes. Recently, our group has developed an image-guided cochlear implant programming (IGCIP) system to simplify the traditionally tedious post-programming procedure and improve hearing outcomes. IGCIP requires image processing techniques to analyze the location of inserted electrode arrays (EAs) with respect to the intra-cochlear anatomy (ICA), and robust and accurate segmentation methods for the ICA are a critical step in the process. We have proposed active shape model (ASM)-based method and deep learning (DL)-based method for this task, and we have observed that DL methods tend to be more accurate than ASM methods while ASM methods tend to be more robust. In this work, we propose a U-Net-like architecture that incorporates ASM segmentation into the network so that it can refine the provided ASM segmentation based on the CT intensity image. Results we have obtained show that the proposed method can achieve the same segmentation accuracy as that of the DL-based method and the same robustness as that of the ASM-based method.
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