37 research outputs found
Optimizing Deep Neural Networks for Single Cell Segmentation
Analysis of live-cell imaging experiments at the resolution of single cells provides exciting insights into the inner workings of biological systems. Advances in biological imaging and computer vision allow for segmentation of natural images with a high degree of accuracy. However, automation of the segmentation pipeline at the single cell resolution remains a challenging task. Complex deep learning models require large, well-annotated datasets that are rarely available in biology. In this research, we explore various methods that optimize state of the art deep learning frameworks, despite limited resources. We trained a large permutation of models to quantify their capacity and to measure the effects of temporal information, spatial awareness and transfer learning on model performance. We find that, although training set size is most impactful in improving model accuracy, we can leverage techniques like spatial awareness and transfer learning to compromise for the lack of data. These insights show that, with an abundance of data, light-weight models can be as performant as their heavy-weight counterparts in cellular analysis
Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
The accurate segmentation and tracking of cells in microscopy image sequences
is an important task in biomedical research, e.g., for studying the development
of tissues, organs or entire organisms. However, the segmentation of touching
cells in images with a low signal-to-noise-ratio is still a challenging
problem. In this paper, we present a method for the segmentation of touching
cells in microscopy images. By using a novel representation of cell borders,
inspired by distance maps, our method is capable to utilize not only touching
cells but also close cells in the training process. Furthermore, this
representation is notably robust to annotation errors and shows promising
results for the segmentation of microscopy images containing in the training
data underrepresented or not included cell types. For the prediction of the
proposed neighbor distances, an adapted U-Net convolutional neural network
(CNN) with two decoder paths is used. In addition, we adapt a graph-based cell
tracking algorithm to evaluate our proposed method on the task of cell
tracking. The adapted tracking algorithm includes a movement estimation in the
cost function to re-link tracks with missing segmentation masks over a short
sequence of frames. Our combined tracking by detection method has proven its
potential in the IEEE ISBI 2020 Cell Tracking Challenge
(http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE
multiple top three rankings including two top performances using a single
segmentation model for the diverse data sets.Comment: 25 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE
ISBI 2020 Cell Tracking Challeng
Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
Deploying off-the-shelf segmentation networks on biomedical data has become
common practice, yet if structures of interest in an image sequence are visible
only temporarily, existing frame-by-frame methods fail. In this paper, we
provide a solution to segmentation of imperfect data through time based on
temporal propagation and uncertainty estimation. We integrate uncertainty
estimation into Mask R-CNN network and propagate motion-corrected segmentation
masks from frames with low uncertainty to those frames with high uncertainty to
handle temporary loss of signal for segmentation. We demonstrate the value of
this approach over frame-by-frame segmentation and regular temporal propagation
on data from human embryonic kidney (HEK293T) cells transiently transfected
with a fluorescent protein that moves in and out of the nucleus over time. The
method presented here will empower microscopic experiments aimed at
understanding molecular and cellular function.Comment: Accepted at MICCAI Workshop on Medical Image Learning with Less
Labels and Imperfect Data, 202