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Item type:Publication, Access status: without files , Nodule detection and generation on chest X-rays: NODE21 challenge(IEEE, 2024-03-26); ; ; ; Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.Publicationtype: Journal Article22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A systematic approach to deep learning-based nodule detection in chest radiographsLung cancer is a serious disease responsible for millions of deaths every year. Early stages of lung cancer can be manifested in pulmonary lung nodules. To assist radiologists in reducing the number of overseen nodules and to increase the detection accuracy in general, automatic detection algorithms have been proposed. Particularly, deep learning methods are promising. However, obtaining clinically relevant results remains challenging. While a variety of approaches have been proposed for general purpose object detection, these are typically evaluated on benchmark data sets. Achieving competitive performance for specific real-world problems like lung nodule detection typically requires careful analysis of the problem at hand and the selection and tuning of suitable deep learning models. We present a systematic comparison of state-of-the-art object detection algorithms for the task of lung nodule detection. In this regard, we address the critical aspect of class imbalance and and demonstrate a data augmentation approach as well as transfer learning to boost performance. We illustrate how this analysis and a combination of multiple architectures results in state-of-the-art performance for lung nodule detection, which is demonstrated by the proposed model winning the detection track of the Node21 competition. The code for our approach is available at https://github.com/FinnBehrendt/node21-submit.Publicationtype: Journal ArticleScopus© Citations 2 25 57 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Real-time motion analysis with 4D deep learning for ultrasound-guided radiotherapy(IEEE, 2023-09-01); ; ; ; Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the target dose. Ultrasound offers direct imaging of tissue in real-time and is considered for image guidance in radiation therapy. Recently, fast volumetric ultrasound has gained traction, but motion analysis with such high-dimensional data remains difficult. While deep learning could bring many advantages, such as fast data processing and high performance, it remains unclear how to process sequences of hundreds of image volumes efficiently and effectively. We present a 4D deep learning approach for real-time motion estimation and forecasting using long-term 4D ultrasound data. Using motion traces acquired during radiation therapy combined with various tissue types, our results demonstrate that long-term motion estimation can be performed markerless with a tracking error of <inline-formula><tex-math notation="LaTeX">$0.35\pm 0.2$</tex-math></inline-formula> mm and with an inference time of less than 5 ms. Also, we demonstrate forecasting directly from the image data up to 900 ms into the future. Overall, our findings highlight that 4D deep learning is a promising approach for motion analysis during radiotherapy.Publicationtype: Journal Article19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Ultrasound Shear Wave Velocity Estimation in a small Field of View via Spatio-Temporal Deep Learning(SPIE, 2023-04-03); ; ; ; A change in tissue stiffness can indicate pathological diseases and therefore supports physicians in diagnosis and treatment. Ultrasound shear wave elastography (US-SWEI) can be used to quantify tissue stiffness by estimating the velocity of propagating shear waves. While a linear US probe with a lateral imaging width of approximately 40 mm is commonly used and US-SWEI has been successfully demonstrated, some clinical applications, such as laparoscopic or endoscopic interventions, require small probes. This limits the lateral image width to the millimeter range and reduces the available information in the US images substantially. In this work, we systematically analyze the effect of a reduced lateral imaging width for shear wave velocity estimation using the conventional time-of-flight (ToF) method and spatio-temporal convolutional neural networks (ST-CNNs). For our study, we use tissue mimicking gelatin phantoms with varying stiffness and resulting shear wave velocities in the range from 3.63 m/s to 7.09 m/s. We find that lateral imaging width has a substantial impact on the performance of ToF, while shear wave velocity estimation with ST-CNNs remains robust. Our results show that shear wave velocity estimation with ST-CNN can even be performed for a lateral imaging width of 2.1 mm resulting in a mean absolute error of 0.81 ± 0.61 m/s.Publicationtype: Conference Paper51 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Optical force estimation for interactions between tool and soft tissues(Macmillan Publishers Limited, part of Springer Nature, 2023-01-10); ; ; Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect approach, interaction forces are derived from the observed deformation, with learning-based methods improving accuracy and real-time capability. However, the relationship between deformation and force is determined by the stiffness of the tissue. Consequently, both deformation and local tissue properties must be observed for an approach applicable to heterogeneous tissue. In this work, we use optical coherence tomography, which can combine the detection of tissue deformation with shear wave elastography in a single modality. We present a multi-input deep learning network for processing of local elasticity estimates and volumetric image data. Our results demonstrate that accounting for elastic properties is critical for accurate image-based force estimation across different tissue types and properties. Joint processing of local elasticity information yields the best performance throughout our phantom study. Furthermore, we test our approach on soft tissue samples that were not present during training and show that generalization to other tissue properties is possible.Publicationtype: Journal ArticleScopus© Citations 1 55 154 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Spatio-temporal deep learning for medical image sequences(2023); In this work, we study and present spatio-temporal deep learning methods for analyzing medical image sequences. We focus on two application scenarios, motion analysis and dynamic elastography, using optical coherence tomography and ultrasound as imaging modalities. Our findings show that deep learning can effectively address end-to-end processing of sequences of medical image data, including sequences of volumetric images.Publicationtype: Doctoral Thesis158 399 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus(Springer, 2022-09); ; ; ; Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume, shape and other parameters like local contrast. However due to limited training data, traditional supervised learning methods often fail to generalize. Existing deep learning methods in paranasal anomaly classification have been used to diagnose at most one anomaly. In our work, we consider three anomalies. Specifically, we employ a 3D CNN to separate maxillary sinus volumes without anomaly from maxillary sinus volumes with anomaly. To learn robust representations from a small labelled dataset, we propose a novel learning paradigm that combines contrastive loss and cross-entropy loss. Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability. We report that optimising with both losses is advantageous over optimising with only one loss. We also find that our training strategy leads to label efficiency. With our method, a 3D CNN classifier achieves an AUROC of 0.85 ± 0.03 while a 3D CNN classifier optimised with cross-entropy loss achieves an AUROC of 0.66 ± 0.1. Our source code is available at https://github.com/dawnofthedebayan/SupConCEMICCAI₂2.Publicationtype: Conference Paper74 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Systematic analysis of volumetric ultrasound parameters for markerless 4D motion tracking(Springer, 2022-05-21); ; ; ; Objectives: Motion compensation is an interesting approach to improve treatments of moving structures. For example, target motion can substantially affect dose delivery in radiation therapy, where methods to detect and mitigate the motion are widely used. Recent advances in fast, volumetric ultrasound have rekindled the interest in ultrasound for motion tracking. We present a setup to evaluate ultrasound based motion tracking and we study the effect of imaging rate and motion artifacts on its performance. Methods: We describe an experimental setup to acquire markerless 4D ultrasound data with precise ground truth from a robot and evaluate different real-world trajectories and system settings toward accurate motion estimation. We analyze motion artifacts in continuously acquired data by comparing to data recorded in a step-and-shoot fashion. Furthermore, we investigate the trade-off between the imaging frequency and resolution. Results: The mean tracking errors show that continuously acquired data leads to similar results as data acquired in a step-and-shoot fashion. We report mean tracking errors up to 2.01 mm and 1.36 mm on the continuous data for the lower and higher resolution, respectively, while step-and-shoot data leads to mean tracking errors of 2.52 mm and 0.98 mm. Conclusions: We perform a quantitative analysis of different system settings for motion tracking with 4D ultrasound. We can show that precise tracking is feasible and additional motion in continuously acquired data does not impair the tracking. Moreover, the analysis of the frequency resolution trade-off shows that a high imaging resolution is beneficial in ultrasound tracking.Publicationtype: Journal ArticleScopus© Citations 1 33 89 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Ultrasound shear wave elasticity imaging with spatio-temporal deep learning(IEEE, 2022-04-19); ; ; ; Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as the shape of the propagating shear wave or push sequence characteristics. We present 3D spatio-temporal CNNs for fast local elasticity estimation from ultrasound data. This approach is based on retrieving elastic properties from shear wave propagation within small local regions. A large training data set is acquired with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05 kPa with various push locations. The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5.01(437) kPa. Furthermore, we estimate local elasticity independent of the push location and can even perform accurate estimates inside the push region. For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa) and on the background of 85.24% (1.64 kPa) compared to a conventional shear wave method. Overall, our method offers fast local estimations of elastic properties with small spatio-temporal window sizes.Publicationtype: Journal ArticleScopus© Citations 5 87 230 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age predictionLesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with UAD. We propose deep learning for UAD in 3D brain MRI considering additional age information. We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction. We use clinical T1-weighted MRIs of 1735 healthy subjects and the publicly available BraTs 2019 data set for our study. Our novel approach significantly improves UAD performance with an AUC of 92.60% compared to an AUC-score of 84.37% using previous approaches without age information.Publicationtype: Conference Paper34