论文标题
用局部约束学习的对象感知的嵌入生物医学图像的实例分割
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
论文作者
论文摘要
自动实例分割是在许多生物医学应用中发生的问题。最先进的方法要么执行语义分割或从检测方法获得的精炼对象边界框。两者都在不同程度上遭受拥挤的对象，合并相邻的对象或抑制有效的对象。在这项工作中，我们通过深层神经网络将嵌入向量分配给每个像素。对网络进行了训练，可以从同一对象输出类似的像素的相似方向的向量，而相邻的对象在嵌入空间中是正交的，这有效地避免了人群中对象的融合。即使在细胞分割（BBBC006 + DSB2018）和叶片分割数据集（CVPPP2017）上，我们的方法即使在细胞分割（BBBC006 + DSB2018）上具有轻巧的骨干网络也会产生最先进的结果。代码和模型权重公开。
Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model weights are public available.
