Abstract：
Myositis is the inflammation of the muscles that can arise from various sources with diverse symptoms and require different treatments. For treatment to achieve optimal results, it is essential to obtain an accurate diagnosis promptly. This paper presents a new supervised segmentation architecture that can efficiently perform precise segmentation and classification of myositis from ultrasound images with few computational resources. The architecture of our model includes a unique encoder-decoder structure that integrates the Bottleneck Transformer (BOT) with a newly developed Residual block named Multi-Conv Ghost switchable bottleneck Residual Block (MCG_RB). This block effectively captures and analyzes ultrasound image input inside the encoder segment at several resolutions. Moreover, the BOT module is a transformer-style attention module designed to bridge the feature gap between the encoding and decoding stages. Furthermore, multi-level features are retrieved using the MCG-RB module, which combines multi-convolution with ghost switchable residual connections of convolutions for both the encoding and decoding stages. The suggested method attains state-of-the-art performance on a benchmark set of myositis ultrasound images across all parameters, including accuracy, precision, recall, dice coefficient, and Jaccard index. Despite its limited training data, the suggested approach demonstrates remarkable generalizability by yielding exceptional results. The proposed model showed a substantial enhancement in accuracy when compared to segmentation state-of-the-art methods such as Unet++, DeepLabV3, and the Duck-Net. The dice coefficient and Jaccard index obtained improvements of up to 3%, 6%, and 7%, respectively, surpassing the other methods.
摘要：
肌炎是肌肉的炎症，可能来自各种来源，具有不同的症状，需要不同的治疗方法。为了达到最佳治疗效果，及时获得准确的诊断至关重要。本文提出了一种新的监督分割架构，可以有效地执行精确的分割和分类从超声图像，很少的计算资源。我们模型的架构包括一个独特的编码器-解码器结构，该结构将瓶颈转换器（BOT）与新开发的名为Multi-ConvGhost可切换瓶颈残差块（MCG_RB）的残差块集成在一起。这个块以若干分辨率有效地捕获和分析编码器段内部的超声图像输入。此外，BOT模块是一个变压器式的注意模块，旨在弥合编码和解码阶段之间的功能差距。此外，使用MCG-RB模块检索多级特征，它将多卷积与卷积的鬼可切换残差连接相结合，用于编码和解码阶段。建议的方法在所有参数的一组基准肌炎超声图像上都达到了最先进的性能，包括准确性，精度,召回，骰子系数,和Jaccard指数。尽管训练数据有限，建议的方法通过产生出色的结果证明了显着的普适性。与Unet++等最先进的分割方法相比，所提出的模型在准确性上有了显著提高，DeepLabV3和Duck-Net。骰子系数和Jaccard指数获得了高达3%的改善，6%,7%,分别,超越其他方法。
