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Filename: drivers/Session_files_driver.php
Line Number: 177
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Function: require_once
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Filename: Session/Session.php
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File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "choices"
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
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Function: require_once
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Filename: models/Detail_model.php
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Function: strpos
File: /var/www/html/application/controllers/Detail.php
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Function: insertAPISummary
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Function: require_once
Severity: 8192
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Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: str_replace
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Function: formatAIDetailSummary
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Filename: controllers/Detail.php
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Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
Line Number: 256
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "usage"
Filename: controllers/Detail.php
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Backtrace:
File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a two-stage deep neural network to achieve fast and accurate soma detection in large-scale and high-resolution whole mouse brain images (more than 1TB). For the first stage, a lightweight Multi-level Cross Classification Network (MCC-Net) is proposed to filter out images without somas and generate coarse candidate images by combining the advantages of the multi convolution layer's feature extraction ability. It can speed up the detection of somas and reduce the computational complexity. For the second stage, to further obtain the accurate locations of somas in the whole mouse brain images, the Scale Fusion Segmentation Network (SFS-Net) is developed to segment soma regions from candidate images. Specifically, the SFS-Net captures multi-scale context information and establishes a complementary relationship between encoder and decoder by combining the encoder-decoder structure and a 3D Scale-Aware Pyramid Fusion (SAPF) module for better segmentation performance. The experimental results on three whole mouse brain images verify that the proposed method can achieve excellent performance and provide the reconstruction of neurons with beneficial information. Additionally, we have established a public dataset named WBMSD, including 798 high-resolution and representative images ( 256 ×256 ×256 voxels) from three whole mouse brain images, dedicated to the research of soma detection, which will be released along with this paper.
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Source |
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http://dx.doi.org/10.1109/TMI.2022.3206605 | DOI Listing |
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