Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes, and scanning modalities. Recently, many convolutional neural networks (CNN) have been designed for segmentation tasks and achieved great success. Few studies, however, have fully considered the sizes of objects, and thus most demonstrate poor performance for small objects segmentation. This can have a significant impact on the early detection of diseases. This paper proposes a Context Axial Reverse Attention Network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. CaraNet applies axial reserve attention (ARA) and channel-wise feature pyramid (CFP) module to dig feature information of small medical object. And we evaluate our model by six different measurement metrics. We test our CaraNet on brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB) segmentation datasets. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects.

翻译：准确且可靠地分割医学图像对疾病诊断和治疗至关重要。由于目标尺寸、形状及扫描模式的广泛多样性，这是一项具有挑战性的任务。近年来，许多卷积神经网络（CNN）被设计用于分割任务并取得了巨大成功。然而，很少有研究充分考虑目标的尺寸，因此大多数方法在小目标分割中表现不佳。这可能会对疾病的早期检测产生显著影响。本文提出了一种上下文轴向反向注意力网络（CaraNet），与近期若干最先进模型相比，该网络可提升对小目标的分割性能。CaraNet应用轴向反向注意力（ARA）和通道级特征金字塔（CFP）模块来挖掘小医学目标的特征信息。我们通过六种不同的度量指标评估模型，并在脑肿瘤（BraTS 2018）和息肉（Kvasir-SEG、CVC-ColonDB、CVC-ClinicDB、CVC-300和ETIS-LaribPolypDB）分割数据集上测试CaraNet。我们的CaraNet获得了排名最高的平均Dice分割精度，结果显示出CaraNet在小医学目标分割中的显著优势。