Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields

@inproceedings{Bortsova2016MitosisDI,
  title={Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields},
  author={Gerda Bortsova and Michael Sterr and Lichao Wang and Fausto Milletari and Nassir Navab and Anika B{\"o}ttcher and Heiko Lickert and Fabian J Theis and Tingying Peng},
  booktitle={MLMI@MICCAI},
  year={2016}
}
Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understood. Asymmetric stem cell divisions have been linked to intestinal stem cell homeostasis and secretory fate commitment. We monitored cell divisions using 4D live cell imaging of cultured intestinal crypts to characterize division modes by means of measurable features such as orientation or shape. A statistical analysis… 

References

SHOWING 1-10 OF 12 REFERENCES
Learning to Detect Cells Using Non-overlapping Extremal Regions
TLDR
A machine learning-based cell detection method applicable to different modalities and state-of-the-art cell detection accuracy is achieved for H&E stained histology, fluorescence, and phase-contrast images.
Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images
TLDR
A novel method for automatic immune cell counting on digitally scanned images of IHC stained slides is presented and demonstrates more effective detection than the existing technique and the result is also in accordance with the human observer's output.
Stem cell self-renewal in intestinal crypt.
You Should Use Regression to Detect Cells
TLDR
It is shown that cells can be detected reliably in images by predicting a monotonous function of the distance to the center of the closest cell, which results in a very simple method, which is easy to implement.
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
TLDR
This work uses deep max-pooling convolutional neural networks to detect mitosis in breast histology images using as context a patch centered on the pixel to classify each pixel in the images.
Adult intestinal stem cells: critical drivers of epithelial homeostasis and regeneration
  • N. Barker
  • Biology
    Nature Reviews Molecular Cell Biology
  • 2014
TLDR
These exciting new insights into the biology of intestinal stem cells have the potential to accelerate the development of stem cell-based therapies and ameliorate cancer treatments.
Landmark detection and coupled patch registration for cardiac motion tracking
TLDR
This paper proposes an automatic framework to select and track a sparse set of distinctive landmarks in the presence of relatively large deformations in order to capture the endocardial motion in cardiac MR sequences and demonstrates that motion tracking using sparse landmarks can outperform conventional motion tracking by a substantial amount.
Hough Forests for Object Detection, Tracking, and Action Recognition
TLDR
Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time that improve the performance of the generalized Hough transform for object detection on a categorical level and extend to new domains such as object tracking and action recognition.
Anatomic-landmark detection using graphical context modelling
TLDR
A graphical model that fully automatically detects Anatomical landmarks in images using a unary potential using a random forest classifier based on local appearance and binary and ternary potentials encoding geometrical context among different landmarks is presented.
...
...