In this work, we utilize a Transformer-based network for precise anatomical landmark detection in chest X-ray images. By combining the strengths of Transformers and UNet architecture, our proposed model achieves robust landmark localization by effectively capturing global context and spatial dependencies. Notably, our method surpasses the current state-of-the-art approaches, exhibiting a significant reduction in Mean Radial Error and a notable improvement in the rate of accurate landmark detection. Each of the landmark points in the labels is presented as a Gaussian heatmap for training the network, using a hybrid loss function, incorporating binary cross-entropy and Dice loss functions, allowing for pixel-wise classification of the heatmaps and segmentation-based training to accurately localize the landmark heatmaps. The promising results obtained highlight the underexplored potential of Transformers in anatomical landmark detection and offer a compelling solution for accurate anatomical landmark detection in chest X-rays. Our work demonstrates the viability of Transformer-based models in addressing the challenges of landmark detection in medical imaging.
The precise placement of catheter tubes and lines is crucial for providing optimal care to critically ill patients. However, the challenge of mispositioning these tubes persists. The timely detection and correction of such errors are extremely important, especially considering the increased demand for these interventions, as seen during the COVID-19 pandemic. Unfortunately, manual diagnosis is prone to error, particularly under stressful conditions, highlighting the necessity for automated solutions. This research addresses this challenge by utilizing deep learning techniques to automatically detect and classify the positions of endotracheal tubes (ETTs) in chest x-ray images. Our approach builds upon recent advancements in deep learning for medical image analysis, providing a sophisticated solution to a critical healthcare challenge. The proposed model achieves remarkable performance, with the area under the ROC scores ranging from 0.961 to 0.993 and accuracy values ranging from 0.961 to 0.999. These results emphasize the effectiveness of the model in accurately classifying ETT positions, highlighting its potential clinical utility. Through this study, we introduce an innovative application of AI in medical diagnostics, with considerations for advancing healthcare practices.
Catheter tubes and lines are one of the most common abnormal findings on a chest x-ray. Misplaced catheters can cause serious complications, such as pneumothorax, cardiac perforation, or thrombosis, and for this reason, assessment of catheter position is of utmost importance. In order to prevent these problems, radiologists usually examine chest x-rays to evaluate their positions after insertion and throughout intensive care. However, this process is both time-consuming and prone to human error. Efficient and dependable automated interpretations have the potential to lower the expenses of surgical procedures, lessen the burden on radiologists, and enhance the level of care for patients. To address this challenge, we have investigated the task of accurate segmentation of catheter tubes and lines in chest x-rays using deep learning models. In this work, we have utilized transfer learning and transformer-based networks where we utilized two different models: a U-Net++-based model with ImageNet pre-training and an efficientnet encoder, which leverages diverse visual features in ImageNet to improve segmentation accuracy, and a transformer-based U-Net architecture due to its capability to handle long-range dependencies in complex medical image segmentation tasks. Our experiments reveal the effectiveness of the U-Net++-based model in handling noisy and artifact-laden images and TransUNET’s potential for capturing complex spatial features. We compare both models using the dice coefficient as the evaluation metric and determine that U-Net++ outperforms TransUNET in terms of these segmentation metrics. Our aim is to achieve more robust and reliable catheter tube detection in chest x-rays, ultimately enhancing clinical decision-making and patient care in critical healthcare settings.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.