Efficient Medical Instrument Detection in 3D Volumetric Ultrasound Data.

Ultrasound-guided procedures have been applied in many clinical therapies, such as cardiac catheterization and regional anesthesia. Medical instrument detection in 3D Ultrasound (US) is highly desired, but the existing approaches are far from real-time performance. Our objective is to investigate an efficient instrument detection method in 3D US for practical clinical use. We propose a novel Multi-dimensional Mixed Network for efficient instrument detection in 3D US, which extracts the discriminating features at 3D full-image level by a 3D encoder, and then applies a specially designed dimension reduction block to reduce the spatial complexity of the feature maps by projecting from 3D space into 2D space. A 2D decoder is adopted to detect the instrument along the specified axes. By projecting the predicted 2D outputs, the instrument is detected or visualized in the 3D volume. Furthermore, to enable the network to better learn the discriminative information, we propose a multi-level loss function to capture both pixel- and image-level differences. We carried out extensive experiments on two datasets for two tasks: (1) catheter detection for cardiac RF-ablation and (2) needle detection for regional anesthesia. Our experiments show that our proposed method achieves a detection error of 2-3 voxels with an efficiency of about 0.12 sec per 3D US volume. The proposed method is 3-8 times faster than the state-of-the-art methods, leading to real-time performance. The results show that our proposed method has significant clinical value for real-time 3D US-guided intervention.