Abstract
Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e.g., cells, glomeruli, and nuclei). Given its outstanding effectiveness in instance detection, it is compelling to consider the application of circle representation for segmenting instance medical objects. In this study, we introduce CircleSnake, a simple end-to-end segmentation approach that utilizes circle contour deformation for segmenting ball-shaped medical objects at the instance level. The innovation of CircleSnake lies in these three areas: (1) It substitutes the complex bounding box-to-octagon contour transformation with a more consistent and rotation-invariant bounding circle-to-circle contour adaptation. This adaptation specifically targets ball-shaped medical objects. (2) The circle representation employed in CircleSnake significantly reduces the degrees of freedom to two, compared to eight in the octagon representation. This reduction enhances both the robustness of the segmentation performance and the rotational consistency of the method. (3) CircleSnake is the first end-to-end deep instance segmentation pipeline to incorporate circle representation, encompassing consistent circle detection, circle contour proposal, and circular convolution in a unified framework. This integration is achieved through the novel application of circular graph convolution within the context of circle detection and instance segmentation. In practical applications, such as the detection of glomeruli, nuclei, and eosinophils in pathological images, CircleSnake has demonstrated superior performance and greater rotation invariance when compared to benchmarks. The code has been made publicly available: this https URL.
Abstract (translated)
近年来，在医学影像中引入了圆表示法，特别设计用于增强实例对象的检测（例如细胞、肾小球和核）。由于其在实例检测方面的出色效果，人们不禁考虑将圆表示法应用于实例分割。在这项研究中，我们引入了CircleSnake，一种简单的端到端实例分割方法，它利用圆轮廓变形在实例级别分割球形医疗物体。圆Snake的创新之处在于这三个方面：（1）它用更一致和旋转不变的圆轮廓到圆轮廓的适应取代了复杂的边界框到八边形轮廓的变换。这个适应特别针对球形医疗物体。（2）圆表示法在圆Snake中显著减少了自由度，与八边形表示法相比，减少了六个自由度。这种减少增强了分割性能的稳健性以及方法旋转一致性的提高。（3）圆Snake是第一个将圆表示法集成到端到端实例分割中的管道，涵盖了一致的圆检测、圆轮廓建议和环状卷积在一个统一的框架中。通过在圆检测和实例分割的背景下应用新颖的环形图卷积，实现了这一集成。在实际应用中，例如病理图像中检测肾小球、核和嗜碱性粒细胞，圆Snake已经表现出与基准测试相比的卓越性能和更高的旋转不变性。代码已公开发布：https:// this URL。
URL
https://arxiv.org/abs/2403.11507
