A Cascade of CNN and LSTM Network with 3D Anchors for Mitotic Cell Detection in 4D Microscopic Image

A Cascade of CNN and LSTM Network with 3D Anchors for Mitotic Cell Detection in 4D Microscopic Image
Title:
A Cascade of CNN and LSTM Network with 3D Anchors for Mitotic Cell Detection in 4D Microscopic Image
Other Titles:
2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Date:
12 May 2019
Citation:
T. Kitrungrotsakul et al., "A Cascade of CNN and LSTM Network with 3D Anchors for Mitotic Cell Detection in 4D Microscopic Image," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 1239-1243. doi: 10.1109/ICASSP.2019.8682326
Abstract:
Mitotic event detection is a fundamental step in investigating of cell behaviors. The event can be used to analyze various diseases, but most mitotic event detections performed previously focused only on two-dimensional (2D) images with time information. Owing to the complex background (normal cells) and mitotic event orientations, the 2D detection methods yield many false positive and false negative results. To solve this problem, we proposed a 2.5 dimensional (2.5D) cascaded end-to-end network combined with 3D anchors for accurate detection of mitotic events in 4D microscopic images. Our proposed network uses a convolutional long short-term memory to handle issues relating to time sequence; this helps to improve the detection accuracy (reduction of false positives). Furthermore, it uses 3D anchors to capture volume information used to address the orientation problem (reduction of false negatives). The experimental results show that the proposed method can achieve higher precision and recall compared with state-of-the-art methods.
License type:
PublisherCopyrights
Funding Info:
Description:
(C) 2019 IEEE.
ISSN:
2379-190X
1520-6149
ISBN:
978-1-4799-8131-1
978-1-4799-8132-8
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