We present a new boundary sensitive framework for polyp segmentation, called
Polyper. Our method is motivated by a clinical approach that seasoned medical
practitioners often leverage the inherent features of interior polyp regions to
tackle blurred boundaries.Inspired by this, we propose explicitly leveraging
polyp regions to bolster the model's boundary discrimination capability while
minimizing computation. Our approach first extracts boundary and polyp regions
from the initial segmentation map through morphological operators. Then, we
design the boundary sensitive attention that concentrates on augmenting the
features near the boundary regions using the interior polyp regions's
characteristics to generate good segmentation results. Our proposed method can
be seamlessly integrated with classical encoder networks, like ResNet-50,
MiT-B1, and Swin Transformer. To evaluate the effectiveness of Polyper, we
conduct experiments on five publicly available challenging datasets, and
receive state-of-the-art performance on all of them. Code is available at
this https URL

我们提出了一种新的针对息肉分割的边界敏感框架 Polyper，该方法通过使用形态操作符从初始分割图中提取边界和息肉区域，设计了边界敏感注意力机制，集中于利用内部息肉区域的特征增强边界附近的特征，以生成良好的分割结果，并在多个数据集上达到了最先进的性能。

边界敏感息肉分割

Polyper: Boundary Sensitive Polyp Segmentation

The task of blood vessel segmentation in microscopy images is crucial for
many diagnostic and research applications. However, vessels can look vastly
different, depending on the transient imaging conditions, and collecting data
for supervised training is laborious. We present a novel deep learning method
for unsupervised segmentation of blood vessels. The method is inspired by the
field of active contours and we introduce a new loss term, which is based on
the morphological Active Contours Without Edges (ACWE) optimization method. The
role of the morphological operators is played by novel pooling layers that are
incorporated to the network's architecture. We demonstrate the challenges that
are faced by previous supervised learning solutions, when the imaging
conditions shift. Our unsupervised method is able to outperform such previous
methods in both the labeled dataset, and when applied to similar but different
datasets. Our code, as well as efficient PyTorch reimplementations of the
baseline methods VesselNN and DeepVess is available on GitHub -
this https URL

使用深度學習的方法，在显微镜图像中实现无监督的血管分割。通过引入一种基于形态学活动轮廓的未标注数据的新损失函数，利用包含于网络体系结构中的新型池化层，提出的无监督方法能够胜过之前的有监督的学习方法，并且在类似但不同的数据集上也能表现出更好的结果。