/cell-segmentation-processing

Post-processing for fluorescent cell segmentation

Primary LanguageJupyter Notebook

Improving Instance Segmentation for Cell Microscopy

Mikhail Papkov, Allan Kustavus, Roberts Oskars Komarovskis

LOTI.05.037 Digital Image Processing, University of Tartu, fall 2019

Instance segmentation is an essential part of a fluorescent microscopy image processing pipeline. Accurate cell segmentation is necessary for the correctness of any downstream analysis such as cell counting and measuring morphological properties. Although segmentation of e.g. DAPI-stained cell nuclei is a well-known task, many modern methods still suffer from unwanted mergers and splits of separate cell instances. Here, we explore various post-processing methods to improve instance segmentation for the given set of segmentation masks produced by a trained neural network.

Data

Fluorescent and brightfield microscopy images of HepG2 cell culture segmented with UNet++

Objective

Improve instance segmentation for brightfield images with respect to the fluorescent segmentation (fluorescent images are much easirer to segment, pixel-wise errors are lower up to 5x as was previously shown). Main error to fix — merge of touching cells into a single object.

Methods

Concavity-based contour splitting

Source: Splitting touching cells based on concave points and ellipse fitting

  • Detect contours based on thresholded segmentation probability map
  • Detect concavity points
  • Fit ellipse with concavity points and contour anchor points
  • Separate touching cells

Watershed

Source: Automated basin delineation from digital elevation models using mathematical morphology

  • Threshold segmentation probability map at 0.9 to get seeds
  • Assure regions: every object should be represented by a seed
  • Run scikit watershed using inversed probability map
  • Separate touching cells by detected contour

Morphology

Run morphological operations (erosion and dilation).

Benchmarking

Methods are evaluated by pixel-wise accuracy, precision, recall, F1-score, object-wise F1-score.

Accuracy Precision Recall PW F1 OW F1 Merges Splits t, s/img
Baseline 0.887 0.816 0.708 0.758 0.327 17869 17159 -
Watershed 0.886 0.817 0.704 0.756 0.335 9299 28833 11.7
Concavity detection 0.885 0.819 0.693 0.751 0.3 6094 33554 8.2