Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to be delineated precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this paper, we present a scale-aware super-resolution network to adaptively segment lesions of various sizes from the low-resolution medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask super-resolution and lesion image super-resolution. The image super-resolution branch will provide more detailed features for the segmentation branch, i.e., the mask super-resolution branch, for fine-grained segmentation. Meanwhile, we introduce scale-aware dilated convolution blocks into the multi-task decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared to other state-of-the-art methods.

翻译：卷积神经网络（CNN）在医学图像分割领域已取得显著进展。然而，由于病灶在尺度和形状上的差异，病灶分割对基于CNN的最先进算法仍是一项挑战。一方面，微小病灶难以从通常分辨率较低的医学图像中精确分割；另一方面，分割大尺寸病灶需依赖大感受野，这进一步加剧了前一个难题。本文提出一种尺度感知的超分辨率网络，用于从低分辨率医学图像中自适应分割不同尺寸的病灶。该网络包含双分支结构，可同时实现病灶掩膜超分辨率与病灶图像超分辨率。其中，图像超分辨率分支为分割分支（即掩膜超分辨率分支）提供更精细的特征，以实现细粒度分割；同时，我们在多任务解码器中引入尺度感知的空洞卷积模块，根据病灶尺寸自适应调整卷积核的感受野。为引导分割分支从更丰富的高分辨率特征中学习，我们提出特征关联模块和尺度关联模块，以增强双分支的多任务学习能力。在多个具有挑战性的病灶分割数据集上，我们提出的网络相较于其他最先进方法实现了持续的性能提升。