Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India.
Department of Pathology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India.
Comput Med Imaging Graph. 2021 Oct;93:101975. doi: 10.1016/j.compmedimag.2021.101975. Epub 2021 Aug 23.
Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet.
图像分割仍然是计算机视觉领域中最重要的任务之一，在医学图像处理中更是如此。图像分割质量是经常考虑的主要指标，而忽略了内存和计算效率，这限制了使用耗电模型进行实际应用。在本文中，我们提出了一种新的框架（Kidney-SegNet），该框架结合了基于注意力的编码器-解码器架构与多孔空间金字塔池化以及高效的维度卷积的有效性。通过在两个公开可用的肾脏和 TNBC 乳腺 H&E 染色组织病理学图像数据集上评估，所提出的 Kidney-SegNet 架构的分割结果被证明优于现有的最先进的深度学习方法。此外，我们的仿真实验还表明，与用于 H&E 染色组织病理学图像的核分割任务的现有深度学习最先进方法相比，我们提出的架构的计算复杂性和内存需求非常高效。我们实现的源代码将在 https://github.com/Aaatresh/Kidney-SegNet 上提供。
