论文标题
潜在单位掩码的无建议的体积实例细分
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
论文作者
论文摘要
这项工作介绍了一种新的无提案实例分割方法，该方法以滑动窗口样式在整个图像中预测的单个现代分割掩码上构建。与相关方法相反，我们的方法同时预测了所有蒙版，一个用于每个像素，从而在整个图像中共同解决任何冲突。具体而言，将重叠掩码的预测组合到签名图的边缘权重中，随后将其分区以同时获得所有最终实例。结果是一种无参数的方法，该方法对噪声非常有力，并优先考虑重叠掩码之间最高共识的预测。所有掩码均根据低维的潜在表示解码，这导致对大型体积图像的应用严格规定的存储器节省。我们测试了具有挑战性的CREMI 2016 NEURON分割基准测试的方法，在该基准中它可以在其中取得竞争力分数。
This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style. In contrast to related approaches, our method concurrently predicts all masks, one for each pixel, and thus resolves any conflict jointly across the entire image. Specifically, predictions from overlapping masks are combined into edge weights of a signed graph that is subsequently partitioned to obtain all final instances concurrently. The result is a parameter-free method that is strongly robust to noise and prioritizes predictions with the highest consensus across overlapping masks. All masks are decoded from a low dimensional latent representation, which results in great memory savings strictly required for applications to large volumetric images. We test our method on the challenging CREMI 2016 neuron segmentation benchmark where it achieves competitive scores.
