Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

By Alejandro F Frangi, Julia Anne Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger. 2018

185 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging, Medical Imaging

How researchers see this paper

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

In Radiology, Nuclear Medicine and Imaging

1. Dirk Vandermeulen
2. Frederik Maes
3. Matthew B. Blaschko
4. Maxim Berman

In Medical Imaging

1. C. Stringer
2. Marius Pachitariu
3. Anne E Carpenter

Citations in the corpus

• Cellpose: a generalist algorithm for cellular segmentation (2020, methodology): “We compared the segmentations performed by Cellpose to segmentations performed by Stardist [17] and Mask-RCNN [15, 16].”
• Deep Learning for Chest X-ray Analysis: A Survey (2021, unknown): “Chen et al. (2018c) proposed a ranked feature extraction and hashing model, while Silva et al. (2020) proposed to use saliency maps as a similarity measure.”
• CellProfiler 4: improvements in speed, utility and usability (2021, methodology): “For CellProfiler 3 we previously released a plugin for NucleAIzer [15], and in the future we hope to investigate integrations with other popular models such as Cellpose [16] and Stardist [17].”
• Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index (2020, methodology): “We categorized all MICCAI 2018 proceedings [35] as indicated by the flowchart in Figure 6.”