Information-theoretic feature detection in ultrasound images

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning

We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultrasound image processing, our previous system could only track abdominal motions under breath-hold. To overcome this limitation, a novel 2D-based image processing method for tracking intra-fraction respiratory motion is proposed. Fifty-seven different anatomical features acquired from 27 sets of 2D ultrasound sequences were used in this study. Three 2D ultrasound sequences were acquired with the robotic ultrasound system from three healthy volunteers. The remaining datasets were provided by the 2015 MICCAI Challenge on Liver Ultrasound Tracking. All datasets were preprocessed to extract the feature point, and a patient-specific motion pattern was extracted by principal component analysis and slow feature analysis (SFA). The tracking finds the most similar frame (or indexed frame) by a k-dimensional-tree-based nearest neighbor search for estimating the tracked object location. A template image was updated dynamically through the indexed frame to perform a fast template matching (TM) within a learned smaller search region on the incoming frame. The mean tracking error between manually annotated landmarks and the location extracted from the indexed training frame is 1.80  ±  1.42 mm. Adding a fast TM procedure within a small search region reduces the mean tracking error to 1.14  ±  1.16 mm. The tracking time per frame is 15 ms, which is well below the frame acquisition time. Furthermore, the anatomical reproducibility was measured by analyzing the location's anatomical landmark relative to the probe; the position-controlled probe has better reproducibility and yields a smaller mean error across all three volunteer cases, compared to the force-controlled probe (2.69 versus 11.20 mm in the superior-inferior direction and 1.19 versus 8.21 mm in the anterior-posterior direction). Our method reduces the processing time for tracking respiratory motion significantly, which can reduce the delivery uncertainty.

Prior knowledge snake segmentation of ultrasound images denoised by J-divergence anisotropy diffusion

BACKGROUND

Applying transrectal ultrasound to robot-assisted laparoscopic radical prostatectomy has attracted attention in recent years, and it is considered as a proper method to provide real-time subsurface anatomic features. A precise registration between the ultrasound equipment and robotic surgical system is necessary, which usually requires a fast and accurate recognition of the registration tool in the ultrasound image.

METHODS

Tissue forceps are chosen as the registration tool. J-divergence anisotropy diffusion and prior knowledge snake segmentation are proposed for the automatic recognition of forceps in ultrasound images.

RESULTS

Simulation, gel tissue phantom experiments and in vitro experiments are carried out. Several evaluation indices are calculated to compare results under different methods.

CONCLUSIONS

The proposed methods are proved to be practicable, reliable and superior to existing ones, with reduced calculation time and higher accuracy.

Symmetry and appearance based automated detection of salient anatomical regions in ultrasound

In this paper we present a method for automated detection of enclosed anatomical regions in ultrasound images by utilizing the coarse shape symmetry as well as relative homogeneity of their sonographic appearance. The proposed method comprises of two steps: First, local phase based filtering [2] is used to detect points in the image which are roughly positioned along the axes of spatial symmetry with respect to structures around them. Secondly, the sonographic 'appearance' and location of these points is used to define a distance-map on the image, which is supplied to a simple fast-marching algorithm in order to provide the final feature detections. The method is robust to ultrasound speckle and works well with or without specialized pre-processing (e.g. speckle-reduction filtering). We illustrate the proposed method with qualitative results on in-vivo Ultrasound images.