Segmented anisotropic ssTEM dataset of neural tissue

By G. Stephan, Fu-Chiang Jan, M. Julien, Cardona Albert, Fetter Richard. 2013

Subfields: Evaluation & Benchmarks, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Evaluation & Benchmarks

1. J. Liew
2. Sanja Fidler
3. Amlan Kar
4. Yunchao Wei
5. Huan Ling
6. Yao Zhao
7. David Acuna
8. Shikui Wei

In Neuroscience

1. Yao Zhao
2. Sanja Fidler

Citations in the corpus

• Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++ (2018, methodology): “For mapping, there is a need to obtain detailed footprints of buildings and roads from aerial/satellite imagery [34], while medical/healthcare domains require automatic methods to precisely outline cells, tissues and other relevant structures [15, 11].”
• Interactive Object Segmentation With Inside-Outside Guidance (2020, methodology): “Over the past few years, we have witnessed a revolutionary advancement in semantic [44, 40, 68, 69, 10, 11, 12, 30, 15, 31] and instance segmentation [25, 36, 8, 60, 13, 65, 34, 4, 9, 43, 29] for different domains, such as general scenes [20, 41, 70], autonomous driving [17, 48, 21], aerial imagery ”
• Fast Interactive Object Annotation With Curve-GCN (2019, methodology): “For cross-domain experiments, we evaluate the generalization capability of our Cityscapes-trained model on the KITTI dataset [13] and four out-of-domain datasets, ADE20K [38], Aerial Rooftop [31], Cardiac MR [30], and ssTEM [14], following Polygon-RNN++ [2].”
• PseudoClick: Interactive Image Segmentation with Click Imitation (2022, methodology): “To evaluate the generalization ability of the proposed method, we conduct crossdomain evaluation on two medical image datasets: BraTS [20] and ssTEM [21].”