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Item type:Publication, Access status: with files , Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks(Wiley, 2025); ; ; Objective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation. Methods: Deep learning techniques have been employed for automatic VS tumor segmentation, including 2D, 2.5D, and 3D UNet-like architectures, which is a specific CNN designed to improve automatic segmentation for medical imaging. Specifically, we introduce a sequential connection where the first UNet's predicted segmentation map is passed to a second complementary network for refinement. Additionally, spatial attention mechanisms are utilized to further guide refinement in the second network. Results: We conducted experiments on both public and private datasets containing contrast-enhanced T1 and high-resolution T2-weighted magnetic resonance imaging (MRI). Across the public dataset, we observed consistent improvements in Dice scores for all variants of 2D, 2.5D, and 3D CNN methods, with a notable enhancement of 8.86% for the 2D UNet variant on T1. In our private dataset, a 3.75% improvement was reported for 2D T1. Moreover, we found that T1 images generally outperformed T2 in VS segmentation. Conclusion: We demonstrate that sequential connection of UNets combined with spatial attention mechanisms enhances VS segmentation performance across state-of-the-art 2D, 2.5D, and 3D deep learning methods. Level of Evidence: 3 Laryngoscope, 2024.Publicationtype: Journal Article16 44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Computer-aided diagnosis of maxillary sinus anomalies : validation and clinical correlation(Wiley, 2024-09); ; ; ; Objective: Computer aided diagnostics (CAD) systems can automate the differentiation of maxillary sinus (MS) with and without opacification, simplifying the typically laborious process and aiding in clinical insight discovery within large cohorts. Methods: This study uses Hamburg City Health Study (HCHS) a large, prospective, long-term, population-based cohort study of participants between 45 and 74 years of age. We develop a CAD system using an ensemble of 3D Convolutional Neural Network (CNN) to analyze cranial MRIs, distinguishing MS with opacifications (polyps, cysts, mucosal thickening) from MS without opacifications. The system is used to find correlations of participants with and without MS opacifications with clinical data (smoking, alcohol, BMI, asthma, bronchitis, sex, age, leukocyte count, C-reactive protein, allergies). Results: The evaluation metrics of CAD system (Area Under Receiver Operator Characteristic: 0.95, sensitivity: 0.85, specificity: 0.90) demonstrated the effectiveness of our approach. MS with opacification group exhibited higher alcohol consumption, higher BMI, higher incidence of intrinsic asthma and extrinsic asthma. Male sex had higher prevalence of MS opacifications. Participants with MS opacifications had higher incidence of hay fever and house dust allergy but lower incidence of bee/wasp venom allergy. Conclusion: The study demonstrates a 3D CNN's ability to distinguish MS with and without opacifications, improving automated diagnosis and aiding in correlating clinical data in population studies. Level of Evidence: 3 Laryngoscope, 134:3927–3934, 2024.Publicationtype: Journal Article20 64 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Self-supervised learning for classifying paranasal anomalies in the maxillary sinus(Springer, 2024-06-08); ; ; ; Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS). Methods: Our approach uses a 3D convolutional autoencoder (CAE) trained in an unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE to reduce reconstruction errors when reconstructing normal maxillary sinus (MS) image. Then, this CAE is applied to an unlabelled dataset to generate coarse anomaly locations by creating residual MS images. Following this, a 3D convolutional neural network (CNN) reconstructs these residual images, which forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images. Results: The proposed SSL technique exhibits superior performance compared to existing generic self-supervised methods, especially in scenarios with limited annotated data. When trained on just 10% of the annotated dataset, our method achieves an area under the precision-recall curve (AUPRC) of 0.79 for the downstream classification task. This performance surpasses other methods, with BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and masked autoencoding using SparK at 0.75. Conclusion: A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly.Publicationtype: Journal Article14 60 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automatic segmentation and detection of Vestibular Schwannoma in MRI by Deep Learning(Thieme, 2024-04-19); ; ; Automatic segmentation and detection of pathologies in MRI by deep learning is an upcoming topic. We introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning. For deep learning and establishing a combined and new CNN model, our evaluations encompass publicly available (n=242) and in-house datasets (n=96) with high resolution MRI of VS. We used contrast enhanced T1- and T2-weighted MRI slices showing the VS. Images were extracted and annotated with the Software Cascade by an otorhinolaryngologist (ORL) subspecialized in otology and a Radiologist. Experiments were run with 2D, 2.5D, and 3D variations. For the calculation 242 publicly available datasets and 96 in-house datasets of high resolution MRI were used. We annotated 251 T1 and 246 T2 images of the in-house dataset and combined them for the experiments. By the calculation with our new combined CNN Model we were able to show, that the highest Dice Score (0,89) was achieved with T1 images in the 3D Model. In regard to the in-house dataset, the calculation with T1 showed a better detection of VS than with T2. In summary, we present a method enhancing CNN-based VS segmentation performance, validated on public and inhouse datasets, ceT1 and hrT2 modalities to improve accuracy of existing models.Publicationtype: Conference Paper13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Validation and correlation with clinical data of a newly developed computer aided diagnostic system for the classification of paranasal anomalies in the maxillary sinus from MRI images(Thieme, 2024-04-19); ; ; Introduction Large scale population studies are used to analyse the rate of finding sinus opacities in cranial MRIs (cMRI). Artificial Intelligence support systems can automate the detection of sinus opacities and reduce the workload of clinicians. We developed and evaluated a Computer Aided Diagnostics system based on a 3D Convolutional Neural Network (3D CNN) that automatically extracts and classifies maxillary sinus (MS) from cMRI. As part of the Hamburg City Health Study, cMRIs of 2619 participants (45-74 years) were recorded for neuroradiological assessment. 1069 participants MS were manually diagnosed for incidental findings by 2 ENT specialists and a ENT-specialized radiologist. The labelled dataset was used to develop and train a 3D CNN that extracts MS from cMRI and classified MS with opacifications (polyps, cyst, mucosal thickening) from MS without opacifications. Association of the two groups with multiple clinical data was tested. We extracted 30 MS volumes from each participants MRI. The 3D CNN dataset included 19215 (59.91%) MS without opacifications, 4815 (15.01%) with mucosal wall inflammations (>2mm), 6315 (19.69%) with polyps, 1185 (3,69%) with cysts, and 540 (1.68%) with polyps/cysts encompassing the entire MS. The evaluation metrics (AUROC: 0.95, sensitivity: 0.85, specificity: 0.90) demonstrated the effectiveness of our approach. Statistically significant associations between the two groups were observed regarding alcohol consumption, BMI, asthma, hay fever and sex. Our 3D CNN showed the ability to classify MS with and without opacifications and automatically diagnose incidental findings, which can enhance the efficiency of uncovering correlations with clinical data in the context of population studies.Publicationtype: Conference Paper6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Motion-compensated OCT imaging of laryngeal tissue(SPIE, 2024-02); ; ; Breda, Philippe C.The increasing incidence of laryngeal carcinomas requires approaches for early diagnosis and treatment. In clinical practice, white light endoscopy of the laryngeal region is typically followed by biopsy under general anesthesia. Thus, image based diagnosis using optical coherence tomography (OCT) has been proposed to study sub-surface tissue layers at high resolution. However, accessing the region of interest requires robust miniature OCT probes that can be forwarded through the working channel of a laryngoscope. Typically, such probes generate A-scans, i.e., single column depth images, which are rather difficult to interpret. We propose a novel approach using the endoscopic camera images to spatially align these A-scans. Given the natural tissue motion and movements of the laryngoscope, the resulting OCT images show a three-dimensional representation of the sub-surface structures, which is simpler to interpret. We present the overall imaging setup and the motion tracking method. Moreover, we describe an experimental setup to assess the precision of the spatial alignment. We study different tracking templates and report root-mean-squared errors of 0.08mm and 0.18mm for sinusoidal and freehand motion, respectively. Furthermore, we also demonstrate the in-vivo application of the approach, illustrating the benefit of spatially meaningful alignment of the A-scans to study laryngeal tissue.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Convolutional transformer network for paranasal anomaly classification in the maxillary sinus(SPIE, 2024-02); ; ; ; Large-scale population studies have examined the detection of sinus opacities in cranial MRIs. Deep learning methods, specifically 3D convolutional neural networks (CNNs), have been used to classify these anomalies. However, CNNs have limitations in capturing long-range dependencies across the low and high level features, potentially reducing performance. To address this, we propose an end-to-end pipeline using a novel deep learning network called ConTra-Net. ConTra-Net combines the strengths of CNNs and self-attention mechanisms of transformers to classify paranasal anomalies in the maxillary sinuses. Our approach outperforms 3D CNNs and 3D Vision Transformer (ViT), with relative improvements in F1 score of 11.68% and 53.5%, respectively. Our pipeline with ConTra-Net could serve as an alternative to reduce misdiagnosis rates in classifying paranasal anomalies.Publicationtype: Conference Paper27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus(Springer, 2023-07-21); ; ; ; Purpose : Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. Methods : We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately localizing the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a strategy which includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a Multiple Instance Ensembling (MIE) prediction method to further boost classification performance. Results : With sampling and MIE, we observe that there is consistent improvement in classification performance of all 3D ResNet and 3D DenseNet architecture with an average AUPRC percentage increase of 21.86 ± 11.92% and 4.27 ± 5.04% by sampling and 28.86 ± 12.80% and 9.85 ± 4.02% by sampling and MIE, respectively. Conclusion : Sampling and MIE can be effective techniques to improve the generalizability of CNNs for paranasal anomaly classification. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy through sampling alongside a novel MIE strategy that proves to be beneficial for paranasal anomaly classification in the MS.Publicationtype: Journal ArticleScopus© Citations 2 27 105 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Tissue classification during needle insertion using self-supervised contrastive learning and optical coherence tomography(2023-07); ; ; 37 78 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Deep-learning-based image acquisition support tool for endoscopic narrow band imaging of the larynx(Thieme, 2023-05-12); ; ; Narrow band imaging (NBI) enables a contrast-enhanced imaging of mucosal blow-vessels. Nowadays NBI is a standard feature in many endoscopes. NBI is increasingly being applies in clinical investigations of the head-neck region. Using flexible laryngoscopes different laryngeal lesions can be investigated in awake patients. NBI enables a better recognition and differentiation of different pathologies than white light endoscopyPublicationtype: Journal Article21