Lung cancer has both high incidence and mortality rates compared to other cancer types. One important factor for improved patient survival is early detection. Deep learning for lung nodule detection has been extensively studied, as a tool to facilitate clinicians with early nodule detection and classification. Many publications are reporting high detection accuracy and several models have been introduced to clinical practice. However, certain models may have reduced performance in real-world clinical practice. In this study, we introduce a method to assess the robustness of lung nodule detection models. Medically relevant image perturbations are used to assess the robustness of these models. The perturbations include noise and motion perturbations, which have been created in consultation with an expert radiologist to ensure the clinical relevance of the artifacts for thoracic computed tomography (CT) scans. The evaluated models demonstrate robustness to clinically relevant noise simulations, but it shows less resilience to motion artifacts in perturbed CT scans. This robustness evaluation method, incorporating simulated relevant artifacts, can be extended for use in other applications involving the analysis of CT scans.
Self-supervised learning (SSL) has become a crucial approach for pre-training deep learning models in natural and medical image analysis. However, applying transformations designed for natural images to three-dimensional (3D) medical data poses challenges. This study explores the efficacy of specific augmentations in the context of self-supervised pre-training for volumetric medical images. A 3D non-contrastive framework is proposed for in-domain self-supervised pre-training on 3D gray-scale thorax CT data, incorporating four spatial and two intensity augmentations commonly used in 3D medical image analysis. The pre-trained models, adapted versions of ResNet-50 and Vision Transformer (ViT)-S, are evaluated on lung nodule classification and lung tumor segmentation tasks. The results indicate a significant impact of SSL, with a remarkable increase in AUC and DSC as compared to training from scratch. For classification, random scalings and random rotations play a fundamental role in achieving higher downstream performance, while intensity augmentations show limited contribution and may even degrade performance. For segmentation, random intensity histogram shifting enhances robustness, while other augmentations have marginal or negative impacts. These findings underscore the necessity of tailored data augmentations within SSL for medical imaging, emphasizing the importance of task-specific transformations for optimal model performance in complex 3D medical datasets.
KEYWORDS: Tumors, Cancer detection, Image segmentation, Pancreas, 3D modeling, Computed tomography, Performance modeling, Pancreatic cancer, Artificial intelligence, Education and training
The development of Artificial Intelligence (AI) for detection and characterization of Pancreatic Ductal Adenocarcinoma (PDAC) is a challenging task, since PDAC data is scarce compared to data of other types of cancer. However, due to the high mortality rate of the disease, early detection is crucial. For this reason, recent work has focused on exploiting indirect pathological features, e.g. dilated bile ducts due to tumor involvement, as an additional input for supportive algorithms. However, the presented methods require manual annotations of several structures in a CT volume, which is a cumbersome task and not feasible in clinical practice. Therefore, this work investigates the automated segmentation of bile ducts to facilitate improved tumor detection by such methods. Using a coarse-to-fine segmentation architecture, the pancreas, pancreatic duct and the common bile duct are segmented from 3D CT-scans. The resulting yet individual segmentations form a primary stage, of which the outputs are supplied as input to a secondary pre-trained U-Net-based PDAC detection algorithm, to ultimately detect tumors. We evaluate the performance of the proposed primary segmentation and secondary detection models on a publicly available test set in terms of mean Dice Similarity Coefficient (DSC). The pancreas, common bile duct and pancreatic duct are segmented with a mean DSC of 0.86, 0.69 and 0.57, respectively. With these segmentations as input, a tumor detection sensitivity of 100% is maintained for the tumor detection model. This continuously high detection sensitivity for tumor detection is comparable to the tumor detection score achieved by using manually annotated structures. This study highlights the benefit of primarily segmenting relevant structures, to use as input for a secondary model for final PDAC detection.
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