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Technical Program
Paper Detail
| Paper: | MA-L3.3 |
| Session: | Biomedical Image Segmentation |
| Time: | Monday, October 9, 10:20 - 10:40 |
| Presentation: |
Lecture
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| Title: |
SEGMENTATION OF DROSOPHILA RNAI FLUORESCENCE IMAGES USING LEVEL SETS |
| Authors: |
Guanglei Xiong; Tsinghua University | | | | | Xiaobo Zhou; Harvard Medical School | | | | | Liang Ji; Tsinghua University | | | | | Pamela Bradley; Harvard Medical School | | | | | Norbert Perrimon; Harvard Medical School | | | | | Stephen T. C. Wong; Harvard Medical School | | |
| Abstract: |
Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Robust automated segmentation of the large volumes of output images generated from image-based screening is much needed for data analyses. In this paper, we propose a new automated segmentation technique to fill the void. The technique consists of two steps: nuclei and cytoplasm segmentation. In the former step, nuclei are extracted, labeled and used as starting points for the latter. A new force obtained from rough segmentation is introduced into the classical level set curve evolution to improve the performance for odd shapes, such as spiky or ruffly cells. A scheme of preventing curves from crossing is proposed to treat the difficulty of segmenting touching cells. We apply it to three types of drosophila cells in RNAi fluorescence images. In all cases, greater than 92% accuracy is obtained. |
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