Motion mode (M-mode) recording is an essential part of echocardiography to
measure cardiac dimension and function. However, the current diagnosis cannot
build an automatic scheme, as there are three fundamental obstructs: Firstly,
there is no open dataset available to build the automation for ensuring
constant results and bridging M-mode echocardiography with real-time instance
segmentation (RIS); Secondly, the examination is involving the time-consuming
manual labelling upon M-mode echocardiograms; Thirdly, as objects in
echocardiograms occupy a significant portion of pixels, the limited receptive
field in existing backbones (e.g., ResNet) composed from multiple convolution
layers are inefficient to cover the period of a valve movement. Existing
non-local attentions (NL) compromise being unable real-time with a high
computation overhead or losing information from a simplified version of the
non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode
echocardiography measurement scheme, contributes three aspects to answer the
problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance
segmentation, to enable consistent results and support the development of an
automatic scheme; 2) propose panel attention, local-to-global efficient
attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS
scheme toward big object detection with global receptive field; 3) develop and
implement AMEM, an efficient algorithm of automatic M-mode echocardiography
measurement enabling fast and accurate automatic labelling among diagnosis. The
experimental results show that RAMEM surpasses existing RIS backbones (with
non-local attention) in PASCAL 2012 SBD and human performances in real-time
MEIS tested. The code of MEIS and dataset are available at
this https URL

提出了 RAMEM，一种实时自动 M 型超声心动图测量方案，包括提供 MEIS 数据集、基于面板注意力的局部到全局高效注意力、以及快速准确的自动标注算法 AMEM，实验证明 RAMEM 在实时 MEIS 测试中超过现有 RIS 骨干网络（具有非局部注意力）和人类表现。