目的:皮肤病变的精确分割是皮肤镜图像分类的关键步骤,这为皮肤科医生诊断皮肤病提供了有力的手段。然而,由于边界模糊,病变与其周围皮肤之间的对比度低,颜色和形状的变化,现有的大多数分割方法在获取感受野和提取图像特征信息方面仍然面临着巨大的挑战。为了解决上述问题,我们构建了一个新的框架,名为SEACU-Net,分析和分割皮肤病变图像。
方法:受U-Net启发,我们利用密集的卷积块来获得更多的判别信息。然后,在每个编码和解码阶段,每次卷积后设计通道和空间挤压和激发层,自适应增强有用的信息特征,并抑制来自不同特征通道的低价值信息。此外,注意力机制被集成到卷积长短期记忆(ConvLSTM)结构中,提高了灵敏度和预测精度。此外,该网络引入了基于二元交叉熵和Jaccard损失的新颖损失,这可以确保更平衡的分割。
结果:所提出的方法应用于ISIC2017和2018年公开图像数据库,然后在骰子中获得更好的性能,Jaccard,和准确性,具有89.11%和87.58%的骰子值,80.50%和78.12%Jaccard值,95.01%,和93.60%的准确度值,分别。
结论:定量和定性实验的结果表明,我们的方法达到了高性能的皮肤病变分割,并可以帮助放射科医生在临床实践中制定放射治疗计划。
OBJECTIVE: Accurate segmentation of skin lesions is a pivotal step in dermoscopy image classification, which provides a powerful means for dermatologists to diagnose skin diseases. However, due to blurred boundaries, low contrast between the lesion and its surrounding skin, and changes in color and shape, most existing segmentation methods still face great challenges in obtaining receptive fields and extracting image feature information. To settle the above issues, we construct a new framework, named SEACU-Net, to analyze and segment skin lesion images.
METHODS: Inspired by the U-Net, we utilize dense convolution blocks to obtain more discriminative information. Then, at each encoding and decoding stage, a channel and spatial squeeze & excitation layer are designed after each convolution, to adaptively enhance useful information features and suppress low-value ones from different feature channels. In addition, the attention mechanism is integrated into the convolutional long short-term memory (ConvLSTM) structure, which improves sensitivity and prediction accuracy. Furthermore, this network introduces a novel loss based on binary cross-entropy and Jaccard losses, which can ensure more balanced segmentation.
RESULTS: The proposed method is applied to the ISIC 2017 and 2018 publicly image databases, then obtains a better performance in Dice, Jaccard, and Accuracy, with 89.11% and 87.58% Dice value, 80.50% and 78.12% Jaccard value, 95.01%, and 93.60% Accuracy value, respectively.
CONCLUSIONS: The results of quantitative and qualitative experiments show that our method reaches high-performance skin lesion segmentation, and can help radiologists make radiotherapy treatment plans in clinical practice.