Real-Time Device Detection with Rotated Bounding Boxes and Its Clinical Application

TY - CONF

T1 - Real-Time Device Detection with Rotated Bounding Boxes and Its Clinical Application

AU - Ma, Ying Liang

AU - Howell, Sandra

AU - Rinaldi, Aldo

AU - Dhanjal, Tarv

AU - Rhode, Kawal S.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Interventional devices and insertable imaging devices such as transesophageal echo (TOE) probes are routinely used in minimally invasive cardiovascular procedures. Detecting their positions and orientations in X-ray fluoroscopic images is important for many clinical applications. Nearly all interventional devices used in cardiovascular procedures contain a wire or wires and are inserted into major blood vessels. In this paper, novel attention mechanisms were designed to guide a convolution neural network (CNN) model to the areas of wires in X-ray images. The first attention mechanism was achieved by using multi-scale Gaussian derivative filters in the first convolutional layer inside the proposed CNN backbone. By combining these multi-scale Gaussian derivative filters together, they can provide a global attention on the wire-like or tube-like structures. Furthermore, the dot-product based attention layer was used to calculate the similarity between the random filter output and the output from the Gaussian derivative filters, which further enhances the attention on the wire-like or tube-like structures. By using both attention mechanisms, a high-performance CNN backbone was created, and it can be plugged into light-weighted CNN models for multiple object detection. An accuracy of 0.88 ± 0.04 was achieved for detecting an echo probe in X-ray images at 58 FPS, which was measured by intersection-over-union (IoU). Based on the detected pose of the echo probe, 3D echo can be fused with live X-ray images to provide a hybrid guidance solution. Codes are available at https://github.com/YingLiangMa/AttWire.

AB - Interventional devices and insertable imaging devices such as transesophageal echo (TOE) probes are routinely used in minimally invasive cardiovascular procedures. Detecting their positions and orientations in X-ray fluoroscopic images is important for many clinical applications. Nearly all interventional devices used in cardiovascular procedures contain a wire or wires and are inserted into major blood vessels. In this paper, novel attention mechanisms were designed to guide a convolution neural network (CNN) model to the areas of wires in X-ray images. The first attention mechanism was achieved by using multi-scale Gaussian derivative filters in the first convolutional layer inside the proposed CNN backbone. By combining these multi-scale Gaussian derivative filters together, they can provide a global attention on the wire-like or tube-like structures. Furthermore, the dot-product based attention layer was used to calculate the similarity between the random filter output and the output from the Gaussian derivative filters, which further enhances the attention on the wire-like or tube-like structures. By using both attention mechanisms, a high-performance CNN backbone was created, and it can be plugged into light-weighted CNN models for multiple object detection. An accuracy of 0.88 ± 0.04 was achieved for detecting an echo probe in X-ray images at 58 FPS, which was measured by intersection-over-union (IoU). Based on the detected pose of the echo probe, 3D echo can be fused with live X-ray images to provide a hybrid guidance solution. Codes are available at https://github.com/YingLiangMa/AttWire.

KW - Attention CNN

KW - Rotated Object Detection

KW - X-ray Imaging

UR - http://www.scopus.com/inward/record.url?scp=85206164855&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-73083-2_9

DO - 10.1007/978-3-031-73083-2_9

M3 - Paper

AN - SCOPUS:85206164855

SP - 83

EP - 93

T2 - 13th International Workshop on Clinical Image-based Procedures: Towards Holistic Patient Models for Personalized Healthcare, CLIP 2024 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

Y2 - 6 October 2024 through 6 October 2024

ER -