论文标题
有效赛车：使用上下文意识到的有效体积细胞分割
EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring
论文作者
论文摘要
荧光显微镜图像中的体积细胞分割对于研究各种细胞过程很重要。从癌细胞分析到胚胎阶段细胞的行为研究的应用。像其他计算机视野一样，最近的方法使用大型卷积神经网络（CNN）或视觉变压器模型（VIT）。由于可用的3D显微镜图像的数量通常在应用中受到限制，因此我们采用了另一种方法，并引入了一个小的CNN进行体积细胞分割。与以前的CNN模型进行细胞分割相比，我们的模型是有效的，并且具有不对称的编码器结构，而解码器中的参数很少。通过转移学习进一步提高了培训效率。此外，我们在执行体积细胞分割切片的同时，在3D图像的z方向上介绍了上下文意识到的伪分化。我们使用细胞跟踪挑战的细胞分割基准的不同3D数据集评估了我们的方法。我们的分割方法可实现顶级结果，而我们的CNN模型的参数数量比其他顶级方法低25倍。代码和预估计的型号可在以下网址找到：https：//github.com/roydenwa/felficed-cell-seg
Volumetric cell segmentation in fluorescence microscopy images is important to study a wide variety of cellular processes. Applications range from the analysis of cancer cells to behavioral studies of cells in the embryonic stage. Like in other computer vision fields, most recent methods use either large convolutional neural networks (CNNs) or vision transformer models (ViTs). Since the number of available 3D microscopy images is typically limited in applications, we take a different approach and introduce a small CNN for volumetric cell segmentation. Compared to previous CNN models for cell segmentation, our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Training efficiency is further improved via transfer learning. In addition, we introduce Context Aware Pseudocoloring to exploit spatial context in z-direction of 3D images while performing volumetric cell segmentation slice-wise. We evaluated our method using different 3D datasets from the Cell Segmentation Benchmark of the Cell Tracking Challenge. Our segmentation method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods. Code and pretrained models are available at: https://github.com/roydenwa/efficient-cell-seg
