3 research outputs found
Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
Chest radiographs are frequently used to verify the correct intubation of
patients in the emergency room. Fast and accurate identification and
localization of the endotracheal (ET) tube is critical for the patient. In this
study we propose a novel automated deep learning scheme for accurate detection
and segmentation of the ET tubes. Development of automatic systems using deep
learning networks for classification and segmentation require large annotated
data which is not always available. Here we present an approach for
synthesizing ET tubes in real X-ray images. We suggest a method for training
the network, first with synthetic data and then with real X-ray images in a
fine-tuning phase, which allows the network to train on thousands of cases
without annotating any data. The proposed method was tested on 477 real chest
radiographs from a public dataset and reached AUC of 0.99 in classifying the
presence vs. absence of the ET tube, along with outputting high quality ET tube
segmentation maps.Comment: Accepted to MICCAI 201
Automatic classification of multiple catheters in neonatal radiographs with deep learning
We develop and evaluate a deep learning algorithm to classify multiple
catheters on neonatal chest and abdominal radiographs. A convolutional neural
network (CNN) was trained using a dataset of 777 neonatal chest and abdominal
radiographs, with a split of 81%-9%-10% for training-validation-testing,
respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground
truth labelling was limited to tagging each image to indicate the presence or
absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical
arterial and venous catheters (UACs, UVCs). The data set included 561 images
containing 2 or more catheters, 167 images with only one, and 49 with none.
Performance was measured with average precision (AP), calculated from the area
under the precision-recall curve. On our test data, the algorithm achieved an
overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989
(0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984)
for UVCs. Performance was similar for the set of 58 test images consisting of 2
or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997
(0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990)
for UVCs. Our network thus achieves strong performance in the simultaneous
detection of these four catheter types. Radiologists may use such an algorithm
as a time-saving mechanism to automate reporting of catheters on radiographs.Comment: 10 pages, 5 figures (+1 suppl.), 2 tables (+2 suppl.). Submitted to
Journal of Digital Imagin
DeepWL: Robust EPID based Winston-Lutz Analysis using Deep Learning and Synthetic Image Generation
Radiation therapy requires clinical linear accelerators to be mechanically
and dosimetrically calibrated to a high standard. One important quality
assurance test is the Winston-Lutz test which localizes the radiation isocentre
of the linac. In the current work we demonstrate a novel method of analysing
EPID based Winston-Lutz QA images using a deep learning model trained only on
synthetic image data.In addition, we propose a novel method of generating the
synthetic WL images and associated ground-truth masks using an optical
ray-tracing engine to fake mega-voltage EPID images. The model called DeepWL
was trained on 1500 synthetic WL images using data augmentation techniques for
180 epochs. The model was built using Keras with a TensorFlow backend on an
Intel Core i5 6500T CPU and trained in approximately 15 hours. DeepWL was shown
to produce ball bearing and multi-leaf collimator field segmentations with a
mean dice coefficient of 0.964 and 0.994 respectively on previously unseen
synthetic testing data. When DeepWL was applied to WL data measured on an EPID,
the predicted mean displacements were shown to be statistically similar to the
Canny Edge detection method. However, the DeepWL predictions for the ball
bearing locations were shown to correlate better with manual annotations
compared with the Canny edge detection algorithm. DeepWL was demonstrated to
analyse Winston-Lutz images with accuracy suitable for routine linac quality
assurance with some statistical evidence that it may outperform Canny Edge
detection methods in terms of segmentation robustness and the resultant
displacement predictions.Comment: 21 pages, 12 figures, 3 table