Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging prob...
| Main Authors: | , , , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM)
2018
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| Online Access: | http://hdl.handle.net/1721.1/115411 |
| _version_ | 1826194876808560640 |
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| author | Shen, Wei Wang, Bin Jiang, Yuan Wang, Yan Yuille, Alan L. |
| author_facet | Shen, Wei Wang, Bin Jiang, Yuan Wang, Yan Yuille, Alan L. |
| author_sort | Shen, Wei |
| collection | MIT |
| description | In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset. |
| first_indexed | 2024-09-23T10:03:14Z |
| format | Technical Report |
| id | mit-1721.1/115411 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:03:14Z |
| publishDate | 2018 |
| publisher | Center for Brains, Minds and Machines (CBMM) |
| record_format | dspace |
| spelling | mit-1721.1/1154112019-04-12T22:55:43Z Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection Shen, Wei Wang, Bin Jiang, Yuan Wang, Yan Yuille, Alan L. In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. 2018-05-16T18:49:42Z 2018-05-16T18:49:42Z 2017-10-01 Technical Report Working Paper Other http://hdl.handle.net/1721.1/115411 en_US CBMM Memo Series;080 application/pdf Center for Brains, Minds and Machines (CBMM) |
| spellingShingle | Shen, Wei Wang, Bin Jiang, Yuan Wang, Yan Yuille, Alan L. Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection |
| title | Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection |
| title_full | Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection |
| title_fullStr | Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection |
| title_full_unstemmed | Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection |
| title_short | Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection |
| title_sort | multi stage multi recursive input fully convolutional networks for neuronal boundary detection |
| url | http://hdl.handle.net/1721.1/115411 |
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