# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI
### Standard Clinical Plane Extraction from Volumetric (3D) Ultrasound Images  
As an alternative to assisting operators in acquiring freehand 2D standard planes, it is also possible to acquire 3D US volumes covering the whole area of interest and then extract the standard planes automatically from them. This approach may simplify the acquisition of specific planes, but it currently faces significant challenges. On the one hand, 3D US is an emerging imaging modality, but it is significantly more expensive and does not provide as good a sampling quality as 2D scans. On the other hand, 3D DL is still a challenging task. It is computationally very expensive, and given the typically limited training data [33] and the large number of parameters of these neural networks, there is a high risk of overfitting. Furthermore, as there is no real-time feedback during the 3D acquisition, if it is performed by a less experienced technician, unexpected situations, such as bad acoustic contact or artifacts, may ruin the 3D acquisition and prevent the system from obtaining the desired planes. However, there is a very intense line of research on plane extraction from 3D volumetric ultrasound images. Once a 3D scan has been acquired with a 3D US probe or by densely scanning an area with a tracked 2D probe, the standard plane can be extracted as a postprocessing step. This approach does not require real-time computation, as long as all the 3D data has been acquired. For instance, the localization of cardiac planes in 3D ultrasound was achieved by [PERSON] et al. [37] with a random forests-based method, and the automatic localization of fetal planes was achieved by landmark alignment [38], regression [39; 40], and classification [41] methods. [PERSON] et al. [42] proposed a reinforcement learning (RL) method to localize standard planes in noisy US volumes. Despite its good performance, this RL-based method had some limitations, as agents learned separately from specific planes. As agents do not communicate with each other, they cannot learn the valuable spatial relationship among planes. [PERSON] et al. [43] went a step further and devised a multi-agent RL framework to localize multiple uterine standard planes in 3D US simultaneously by establishing communication among agents and a collaborative strategy to learn the spatial relationship among standard planes effectively. Besides, they adopted a one-shot neural architecture search [44] to obtain the optimal agent for each plane. This architecture uses gradient-based search using a Differentiable Architecture Sampler [45], which may make the training more stable and faster. [PERSON] et al. [40] used a CNN to regress a rigid transformation iteratively for localizing TC and TV planes in 3D fetal ultrasound. [PERSON] et al. [46] evolved this idea and combined it with the deep RL approach in [PERSON]'s work to build a deep Q-network-based agent that explores the 3D volume with 6 DOF to locate the desired plane. Results are only validated with a simple phantom. [PERSON] et al. [47] proposed a fully connected NN with seven layers trained to map 2D slice images to their 3D coordinate parameters from an ultrasound volume obtained by a GE ABUS (Automated Breast Ultrasound System). Each slice is described by a pair of translation and rotation parameters that label every possible image in the dataset. However, the model does not generalize the mapping and must be trained from scratch for each new patient. Therefore, it can be seen as image-based indexing that can be used to check slice position and orientation. [PERSON] et al. [48] also proposed a DL method to extract standard fetal head planes from 3D-head volumes with similar quality to operator-selected planes. In [49], [PERSON] et al. propose a novel deep neural network architecture for data-driven, non-rigid point-set registration, which they call Free Point Transformer (FPT).