Yuanwei Li

Amir Alansary, Loic Le Folgoc, Ghislain Vaillant et al.

We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to perform biometric measurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.

Matthew Sinclair, Christian F. Baumgartner, Jacqueline Matthew et al.

Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model's performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers.

Yuanwei Li

Initializing optical flow field by either sparse descriptor matching or dense patch matches has been proved to be particularly useful for capturing large displacements. In this paper, we present a pyramidal gradient matching approach that can provide dense matches for highly accurate and efficient optical flow estimation. A novel contribution of our method is that image gradient is used to describe image patches and proved to be able to produce robust matching. Therefore, our method is more efficient than methods that adopt special features (like SIFT) or patch distance metric. Moreover, we find that image gradient is scalable for optical flow estimation, which means we can use different levels of gradient feature (for example, full gradients or only direction information of gradients) to obtain different complexity without dramatic changes in accuracy. Another contribution is that we uncover the secrets of limited PatchMatch through a thorough analysis and design a pyramidal matching framework based these secrets. Our pyramidal matching framework is aimed at robust gradient matching and effective to grow inliers and reject outliers. In this framework, we present some special enhancements for outlier filtering in gradient matching. By initializing EpicFlow with our matches, experimental results show that our method is efficient and robust (ranking 1st on both clean pass and final pass of MPI Sintel dataset among published methods).