1,170 research outputs found
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and robustness. In the meanwhile, existing approaches usually deal
with these two tasks independently, which would neglect the close relatedness
of them. In this paper, we present a novel method of sibling fully
convolutional network with prior objectness interaction (called SFCN-OPI) to
tackle the two tasks simultaneously and interactively using a unified
end-to-end framework. Specifically, the sibling FCN branches share features in
earlier layers while holding respective higher layers for specific tasks. More
importantly, the detection branch outputs the objectness prior which
dynamically interacts with the fine-grained classification sibling branch
during the training and testing processes. With this mechanism, the
fine-grained classification successfully focuses on regions with high
confidence of nuclei existence and outputs the conditional probability, which
in turn benefits the detection through back propagation. Extensive experiments
on colon cancer histology images have validated the effectiveness of our
proposed SFCN-OPI and our method has outperformed the state-of-the-art methods
by a large margin.Comment: Accepted at AAAI 201
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
- …