QuPath

Noise2Void (N2V) is a self-supervised deep learning method for fluorescence microscopy image denoising that requires only noisy images — no clean ground truth needed. Uses a blind-spot convolutional n

3 shared topics • 1 shared operation

CARE (Content-Aware Image Restoration) is a deep learning framework for fluorescence microscopy image enhancement using convolutional neural networks. Restores noisy, low-SNR microscopy images through

2 shared topics • 2 shared operations

Cellpose — generalist deep learning algorithm for cellular and nuclear segmentation in microscopy images. Provides GPU-accelerated 2D and 3D instance segmentation using gradient flow representations,

2 shared topics • 1 shared operation

DeepCell — deep learning library for single-cell analysis of biological images using TensorFlow. Provides pretrained models for cell segmentation including Mesmer (multiplexed tissue imaging), nuclear

2 shared topics • 1 shared operation

ZeroCostDL4Mic is a Google Colab-based platform providing zero-cost deep learning for fluorescence microscopy image analysis. Supports segmentation (U-Net, StarDist, Cellpose), denoising (Noise2Void,

2 shared topics • 1 shared operation