Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels

• URL: http://arxiv.org/abs/2308.07717v1
• Date: Tue, 15 Aug 2023 11:50:57 GMT
• Title: Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels
• Authors: Ching-Hsun Tseng, Shao-Ju Chien, Po-Shen Wang, Shin-Jye Lee, Wei-Huan Hu, Bin Pu, and Xiao-jun Zeng
• Abstract summary: Motion mode (M-mode) recording is an essential part of echocardiography to measure cardiac dimension and function. There is no open dataset available to build the automation for ensuring constant results. Currently, the examination is involving the time-consuming manual labelling upon M-mode echocardiograms.
• Score: 8.745381510003666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 https://github.com/hanktseng131415go/RAME.

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