ArticleMedical image analysis2020
HeadLocNet: Deep convolutional neural networks for accurate classification and multi-landmark localization of head CTs.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Cross-Subject Registration-Based Augmentation: Alleviating Anatomical Misalignment in Trauma CT for Robust Hemorrhage Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Dual-branch collaborative GAN with multi-scale CBAM and anatomical topology coding for enhanced early HCC detection in CT.Scientific reports · 2025Article
- Deep learning-based automated scan plane positioning for brain magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Cochlear Implant Fold Detection in Intra-operative CT Using Weakly Supervised Multi-task Deep Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Validation of active shape model techniques for intracochlear anatomy segmentation in computed tomography images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automatic Segmentation of Intracochlear Anatomy in MR Images Using a Weighted Active Shape Model.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Fully automated preoperative segmentation of temporal bone structures from clinical CT scans.Scientific reports · 2021Article
- Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network.Computational intelligence and neuroscience · 2021Article
- Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images Using a Local Attention-Based Graph Convolution Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Cochlear implants (CIs) are used to treat subjects with hearing loss. In a CI surgery, an electrode array is inserted into the cochlea to stimulate auditory nerves. After surgery, CIs need to be programmed. Studies have shown that the cochlea-electrode spatial relationship derived from medical images can guide CI programming and lead to significant improvement in hearing outcomes. We have developed a series of algorithms to segment the inner ear anatomy and localize the electrodes. But, because clinical head CT images are acquired with different protocols, the field of view and orientation of the image volumes vary greatly. As a consequence, visual inspection and manual image registration to an atlas image are needed to document their content and to initialize intensity-based registration algorithms used in our processing pipeline. For large-scale evaluation and deployment of our methods these steps need to be automated. In this article we propose to achieve this with a deep convolutional neural network (CNN) that can be trained end-to-end to classify a head CT image in terms of its content and to localize landmarks. The detected landmarks can then be used to estimate a point-based registration with the atlas image in which the same landmark set's positions are known. We achieve 99.5% classification accuracy and an average localization error of 3.45 mm for 7 landmarks located around each inner ear. This is better than what was achieved with earlier methods we have proposed for the same tasks.
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Registered trials
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