9 citations · 9 across the 2 of their papers we have counts for
4 papers
Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images
Steffen Wolf, Manan Lalit, Henry Westmacott +2
Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the sp…
Embedding-based Instance Segmentation in Microscopy
Manan Lalit, Pavel Tomancak, Florian Jug
Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications. In the natural image domain, spatial embedding-base…
Fully Unsupervised Probabilistic Noise2Void
Mangal Prakash, Manan Lalit, Pavel Tomancak +2
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing. A new category of DL methods such…
Leveraging Self-supervised Denoising for Image Segmentation
Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit +3
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copiou…