Cochlear implants (CIs) are surgically implanted neural prosthetic devices to treat severe-to-profound hearing loss. Accurately localizing the CI electrodes relative to the intracochlear anatomy structures (ICAS) in the post-implantation CT (Post-CT) images of the CI recipients can help audiologists with the post-programming of the CIs. Localizing the electrodes and segmenting the ICAS in the Post-CT images are challenging due to the limited image resolution and the strong artifacts produced by the metallic electrodes. Currently, the most accurate approach to determine the physical relationship between the electrodes and the ICAS is to localize the electrodes in the Post-CT image, segment the ICAS in the pre-implantation CT (Pre-CT) image of the CI recipient, and register the two images. Here we propose a 3D multi-task network to remove the artifacts, segment the ICAS, and localize the electrodes in the Post-CT images simultaneously. Our network is trained with a small image set and achieves comparable segmentation results and encouraging electrode localization results compared to the current state-of-the-art methods. As our method does not require the Pre-CT images, it provides the audiologist with information that guides the programming process even for patients for whom these images are not available.
Cochlear implants (CIs) are surgically implanted neural prosthetic devices used to treat severe-to-profound hearing loss. Our group has developed Image-Guided Cochlear Implant Programming (IGCIP) techniques to assist audiologists with the configuration of the implanted CI electrodes. CI programming is sensitive to the spatial relationship between the electrodes and intra cochlear anatomy (ICA) structures. We have developed algorithms that permit determining the position of the electrodes relative to the ICA structure using pre- and post-implantation CT image pairs. However, these do not extend to CI recipients for whom pre-implantation CT (Pre-CT) images are not available because post-implantation CT (Post-CT) images are affected by strong artifacts introduced by the metallic implant. Recently, we proposed an approach that uses conditional generative adversarial nets (cGANs) to synthesize Pre-CT images from Post-CT images. This permits to use algorithms designed to segment Pre-CT images even when these are not available. We have shown that it substantially and significantly improves the results obtained with our previous published methods that segment post- CT images directly. Here we evaluate the effect of this new approach on the final output of our IGCIP techniques, which is the configuration of the CI electrodes, by comparing configurations of the CI electrodes obtained using the real and the synthetic Pre-CT images. In 22/87 cases synthetic image lead to the same results as the real images. Because more than one configuration may lead to equivalent neural stimulation patterns, visual assessment of solutions is required to compare those that differ. This study is ongoing.
Lung cancer stands as the deadliest cancer worldwide, and early detection of pulmonary nodules is the focus of many studies to enhance the survival rate. As with many diseases, deep learning is becoming a commonly used technique for computer-aided diagnosis (CAD) in detecting lung nodules. Most lung CAD systems rely on a detection module followed by a false positive (FP) reduction module (FPR); however, FPR removes FPs as well as true positives (TPs). Thus, as a tradeoff, in order to retain high sensitivity, a large number of FPs remain. In our experience, small pulmonary vessels have been the primary source of FPs. Hence, we propose an additional module cascaded on normal FPR module to specifically reduce the number of FPs due to pulmonary vessel. Utilizing a 3D deep learning architecture, we find that the inclusion of various fields of view (FOVs) improves the accuracy of the chosen model. We explore the impact of the selection of the FOVs, the method used to integrate the features from each FOV, and using the FOV as a data augmentation method. We show that this vessel specific FPR module significantly improves the CAD system’s FP rate while only sacrificing 5% of the previously achieved sensitivity.
Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to treat patients with hearing loss. For CI recipients, sound bypasses the natural transduction mechanism and directly stimulates the neural regions, thus creating a sense of hearing. Post-operatively, CIs need to be programmed. Traditionally, this is done by an audiologist who is blind to the positions of the electrodes relative to the cochlea and only relies on the subjective response of the patient. Multiple programming sessions are usually needed, which can take a frustratingly long time. We have developed an imageguided cochlear implant programming (IGCIP) system to facilitate the process. In IGCIP, we segment the intra-cochlear anatomy and localize the electrode arrays in the patient’s head CT image. By utilizing their spatial relationship, we can suggest programming settings that can significantly improve hearing outcomes. To segment the intra-cochlear anatomy, we use an active shape model (ASM)-based method. Though it produces satisfactory results in most cases, sub-optimal segmentation still happens. As an alternative, herein we explore using a deep learning method to perform the segmentation task. Large image sets with accurate ground truth (in our case manual delineation) are typically needed to train a deep learning model for segmentation but such a dataset does not exist for our application. To tackle this problem, we use segmentations generated by the ASM-based method to pre-train the model and fine-tune it on a small image set for which accurate manual delineation is available. Using this method, we achieve better results than the ASM-based method.
Cochlear implants (CIs) are standard treatment for patients who experience sensorineural hearing loss. Although these devices have been remarkably successful at restoring hearing, it is rare that they permit to achieve natural fidelity and many patients experience poor outcomes. Our group has developed image-guided CI programming techniques (IGCIP), in which image analysis techniques are used to locate the intracochlear position of CI electrodes to determine patient-customized settings for the CI processor. Clinical studies have shown that IGCIP leads to significantly improved outcomes. A crucial step is the localization of the electrodes, and rigorously quantifying the accuracy of our algorithms requires dedicated datasets. We discuss the creation of a ground truth dataset for electrode position and its use to evaluate the accuracy of our electrode localization techniques. Our final ground truth dataset includes 30 temporal bone specimens that were each implanted with one of four different types of electrode array by an experienced CI surgeon. The arrays were localized in conventional CT images using our automatic methods and manually in high-resolution μCT images to create the ground truth. The conventional and μCT images were registered to facilitate comparison between automatic and ground truth electrode localization results. Our technique resulted in mean errors of 0.13 mm in localizing the electrodes across 30 cases. Our approach successfully permitted characterizing the accuracy of our methods, which is critical to understand their limitations for use in IGCIP.
Cochlear implants (CIs) are standard treatment for patients who experience sensorineural hearing loss. Although these devices have been remarkably successful at restoring hearing, it is rare to achieve natural fidelity, and many patients experience poor outcomes. Our group has developed image-guided CI programming techniques (IGCIP), in which image analysis techniques are used to locate the intra-cochlear position of CI electrodes to determine patient-customized settings for the CI processor. Clinical studies have shown that IGCIP leads to significantly improved outcomes. A crucial step is the localization of the electrodes, and rigorously quantifying the accuracy of our algorithms requires dedicated datasets. In this work, we discuss the creation of a ground truth dataset for electrode position and its use to evaluate the accuracy of our electrode localization techniques. Our final ground truth dataset includes 26 temporal bone specimens that were each implanted with one of four different types of electrode array by an experienced Otologist. The arrays were localized in conventional CT images using our automatic methods and manually in high resolution μCT images to create the ground truth. The conventional and μCT images were registered to facilitate comparison between automatic and ground truth electrode localization results. Our technique resulted in mean errors of 0.13mm in localizing the electrodes across 26 cases. Our approach successfully permitted characterizing the accuracy of our methods, which is critical to understand their limitations for use in IGCIP.
Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.
Cochlear implants (CIs) are neural prostheses that restore hearing using an electrode array implanted in the cochlea. After implantation, the CI processor is programmed by an audiologist. One factor that negatively impacts outcomes and can be addressed by programming is cross-electrode neural stimulation overlap (NSO). We have proposed a system to assist the audiologist in programming the CI that we call image-guided CI programming (IGCIP). IGCIP permits using CT images to detect NSO and recommend deactivation of a subset of electrodes to avoid NSO. We have shown that IGCIP significantly improves hearing outcomes. Most of the IGCIP steps are robustly automated but electrode configuration selection still sometimes requires manual intervention. With expertise, distance-versus-frequency curves, which are a way to visualize the spatial relationship learned from CT between the electrodes and the nerves they stimulate, can be used to select the electrode configuration. We propose an automated technique for electrode configuration selection. A comparison between this approach and one we have previously proposed shows that our method produces results that are as good as those obtained with our previous method while being generic and requiring fewer parameters.
Cochlear implants (CIs) are surgically implantable neuroprosthetic devices used to treat profound hearing loss. Recent literature indicates that there is a correlation between the final intracochlear positioning of the CI electrode arrays and the ultimate hearing outcome of the patient, indicating that further studies to better understand the relationship between electrode position and outcomes could have significant implications for future surgical techniques, array design, and processor programming methods. Postimplantation high-resolution computed tomography (CT) imaging is the best modality for localizing electrodes and provides the resolution necessary to visually identify electrode position, although with an unknown degree of accuracy depending on image acquisition parameters, like the hounsfield unit (HU) range of reconstruction, orientation, radiation dose, and image resolution. We report on the development of a phantom and on its use to study how four acquisition parameters, including image resolution and HU range of reconstruction, affect how accurately the true position of the electrodes can be found in a dataset of CT scans acquired from multiple helical and cone beam scanners. We also show how the phantom can be used to evaluate the effect of acquisition parameters on automatic electrode localization techniques.
Cochlear implants (CIs) are used to treat patients with severe-to-profound hearing loss. In surgery, an electrode array is implanted in the cochlea. After implantation, the CI processor is programmed by an audiologist. One factor that negatively impacts outcomes and can be addressed by programming is cross-electrode neural stimulation overlap (NSO). In the recent past, we have proposed a system to assist the audiologist in programming the CI that we call Image-Guided CI Programming (IGCIP). IGCIP permits using CT images to detect NSO and recommend which subset of electrodes should be active to avoid NSO. In an ongoing clinical study, we have shown that IGCIP leads to significant improvement in hearing outcomes. Most of the IGCIP steps are robustly automated but electrode configuration selection still sometimes requires expert intervention. With expertise, Distance-Vs-Frequency (DVF) curves, which are a way to visualize the spatial relationship learned from CT between the electrodes and the nerves they stimulate, can be used to select the electrode configuration. In this work, we propose an automated technique for electrode configuration selection. It relies on matching new patients’ DVF curves to a library of DVF curves for which electrode configurations are known. We compare this approach to one we have previously proposed. We show that, generally, our new method produces results that are as good as those obtained with our previous one while being generic and requiring fewer parameters.
Cochlear Implants (CIs) are surgically implantable neural prosthetic devices used to treat profound hearing loss. Recent literature indicates that there is a correlation between the positioning of the electrode array within the cochlea and the ultimate hearing outcome of the patient, indicating that further studies aimed at better understanding the relationship between electrode position and outcomes could have significant implications for future surgical techniques, array design, and processor programming methods. Post-implantation high resolution CT imaging is the best modality for localizing electrodes and provides the resolution necessary to visually identify electrode position, albeit with an unknown degree of accuracy depending on image acquisition parameters, like the HU range of reconstruction, radiation dose, and resolution of the image. In this paper, we report on the development of a phantom that will both permit studying which CT acquisition parameters are best for accurately identifying electrode position and serve as a ground truth for evaluating how different electrode localization methods perform when using different CT scanners and acquisition parameters. We conclude based on our tests that image resolution and HU range of reconstruction strongly affect how accurately the true position of the electrode array can be found by both experts and automatic analysis techniques. The results presented in this paper demonstrate that our phantom is a versatile tool for assessing how CT acquisition parameters affect the localization of CIs.
Cochlear implants (CIs) are neural prosthetics for treating severe-to-profound hearing loss. Our group has developed an
image-guided cochlear implant programming (IGCIP) system that uses image analysis techniques to recommend patientspecific
CI processor settings to improve hearing outcomes. One crucial step in IGCIP is the localization of CI electrodes
in post-implantation CTs. Manual localization of electrodes requires time and expertise. To automate this process, our
group has proposed automatic techniques that have been validated on CTs acquired with scanners that produce images
with an extended range of intensity values. However, there are many clinical CTs acquired with a limited intensity range.
This limitation complicates the electrode localization process. In this work, we present a pre-processing step for CTs with
a limited intensity range and extend the methods we proposed for full intensity range CTs to localize CI electrodes in CTs
with limited intensity range. We evaluate our method on CTs of 20 subjects implanted with CI arrays produced by different
manufacturers. Our method achieves a mean localization error of 0.21mm. This indicates our method is robust for
automatic localization of CI electrodes in different types of CTs, which represents a crucial step for translating IGCIP
from research laboratory to clinical use.
Cochlear implants (CIs) are neural prostheses that restore hearing by stimulating auditory nerve pathways within the cochlea using an implanted electrode array. Research has shown when multiple electrodes stimulate the same nerve pathways, competing stimulation occurs and hearing outcomes decline. Recent clinical studies have indicated that hearing outcomes can be significantly improved by using an image-guided active electrode set selection technique we have designed, in which electrodes that cause competing stimulation are identified and deactivated. In tests done to date, an expert is needed to perform the electrode selection step with the assistance of a method to visualize the spatial relationship between electrodes and neural sites determined using image analysis techniques. We propose to automate the electrode selection step by optimizing a cost function that captures the heuristics used by the expert. Further, we propose an approach to estimate the values of parameters used in the cost function using an existing database of expert electrode selections. We test this method with different electrode array models from three manufacturers. Our automatic approach generates acceptable active electrode sets in 98.3% of the subjects tested. This approach represents a crucial step toward clinical translation of our image-guided CI programming system.
Cochlear implants (CIs) are neural prosthetics that stimulate the auditory nerve pathways within the cochlea using an implanted electrode array to restore hearing. After implantation, the CI is programmed by an audiologist who determines which electrodes are active, i.e., the electrode configuration, and selects other stimulation settings. Recent clinical studies by our group have shown that hearing outcomes can be significantly improved by using an image-guided electrode configuration selection technique we have designed. Our goal in this work is to automate the electrode configuration selection step with the long term goal of developing a fully automatic system that can be translated to the clinic. Until now, the electrode configuration selection step has been performed by an expert with the assistance of image analysis-based estimates of the electrode-neural interface. To automatically determine the electrode configuration, we have designed an optimization approach and propose the use of a cost function with feature terms designed to interpret the image analysis data in a similar fashion as the expert. Further, we have designed an approach to select parameters in the cost function using our database of existing electrode configuration plans as training data. The results we present show that our automatic approach results in electrode configurations that are better or equally as good as manually selected configurations in over 80% of the cases tested. This method represents a crucial step towards clinical translation of our image-guided cochlear implant programming system.
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