Two-level Training of a 3d U-Net for Accurate Segmentation of the Intra-cochlear Anatomy in Head CTs with Limited Ground Truth Training Data

Super-resolution segmentation network for inner-ear tissue segmentation

Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain μCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.

Validation of active shape model techniques for intracochlear anatomy segmentation in computed tomography images

Purpose

Cochlear implants (CIs) have been shown to be highly effective restorative devices for patients suffering from severe-to-profound hearing loss. Hearing outcomes with CIs depend on electrode positions with respect to intracochlear anatomy. Intracochlear anatomy can only be directly visualized using high-resolution modalities, such as micro-computed tomography (μ CT ), which cannot be used in vivo. However, active shape models (ASM) have been shown to be robust and effective for segmenting intracochlear anatomy in large scale datasets of patient computed tomographies (CTs). We present an extended dataset of μ CT specimens and aim to evaluate the ASM's performance more comprehensively than has been previously possible.

Approach

Using a dataset of 16 manually segmented cochlea specimens on μ CTs , we found parameters that optimize mean CT segmentation performance and then evaluate the effect of library size on the ASM. The optimized ASM was further evaluated on a clinical dataset of 134 CT images to assess method reliability.

Results

Optimized parameters lead to mean CT segmentation performance to 0.36 mm point-to-point error, 0.10 mm surface error, and 0.83 Dice score. Larger library sizes provide diminishing returns on segmentation performance and total variance captured by the ASM. We found our method to be clinically reliable with the main performance limitation that was found to be the candidate search process rather than model representation.

Conclusions

We have presented a comprehensive validation of the ASM for use in intracochlear anatomy segmentation. These results are critical to understand the limitations of the method for clinical use and for future development.

Research and application progress in deep learning in otology

With the optimization of deep learning algorithms and the accumulation of medical big data, deep learning technology has been widely applied in research in various fields of otology in recent years. At present, research on deep learning in otology is combined with a variety of data such as endoscopy, temporal bone images, audiograms, and intraoperative images, which involves diagnosis of otologic diseases (including auricular malformations, external auditory canal diseases, middle ear diseases, and inner ear diseases), treatment (guiding medication and surgical planning), and prognosis prediction (involving hearing regression and speech learning). According to the type of data and the purpose of the study (disease diagnosis, treatment and prognosis), the different neural network models can be used to take advantage of their algorithms, and the deep learning can be a good aid in treating otologic diseases. The deep learning has a good applicable prospect in the clinical diagnosis and treatment of otologic diseases, which can play a certain role in promoting the development of deep learning combined with intelligent medicine.

Hybrid active shape and deep learning method for the accurate and robust segmentation of the intracochlear anatomy in clinical head CT and CBCT images

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.

Artificial intelligence in radiotherapy: a technological review

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.