59 papers with code • 8 benchmarks • 16 datasets
Cell Segmentation is a task of splitting a microscopic image domain into segments, which represent individual instances of cells. It is a fundamental step in many biomedical studies, and it is regarded as a cornerstone of image-based cellular research. Cellular morphology is an indicator of a physiological state of the cell, and a well-segmented image can capture biologically relevant morphological information.
In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data.
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
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.
Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation.