Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

Yao, Hailong; Chang, Jenghwa

doi:10.4236/ijmpcero.2021.102008

Cited by 2 publications

(3 citation statements)

References 22 publications

(20 reference statements)

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“… LEAP (Estimates Animal Poses): LEAP's pose estimation architecture is based on deep learning and uses a 15-layer convolutional neural network to predict the positions of animal body parts 40 . U-Net: U-Net is a convolutional neural network (CNN) architecture with 23 layers and a "U"-shaped format 41 . The presence of both an encoder and a decoder in this architecture helps it address complex tasks such as posture classification 42 , 43 .…”

Section: Methodsmentioning

confidence: 99%

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Deep learning pose detection model for sow locomotion

de Paula,

de Sousa,

Sarmiento

et al. 2024

Sci Rep

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Lameness affects animal mobility, causing pain and discomfort. Lameness in early stages often goes undetected due to a lack of observation, precision, and reliability. Automated and non-invasive systems offer precision and detection ease and may improve animal welfare. This study was conducted to create a repository of images and videos of sows with different locomotion scores. Our goal is to develop a computer vision model for automatically identifying specific points on the sow's body. The automatic identification and ability to track specific body areas, will allow us to conduct kinematic studies with the aim of facilitating the detection of lameness using deep learning. The video database was collected on a pig farm with a scenario built to allow filming of sows in locomotion with different lameness scores. Two stereo cameras were used to record 2D videos images. Thirteen locomotion experts assessed the videos using the Locomotion Score System developed by Zinpro Corporation. From this annotated repository, computational models were trained and tested using the open-source deep learning-based animal pose tracking framework SLEAP (Social LEAP Estimates Animal Poses). The top-performing models were constructed using the LEAP architecture to accurately track 6 (lateral view) and 10 (dorsal view) skeleton keypoints. The architecture achieved average precisions values of 0.90 and 0.72, average distances of 6.83 and 11.37 in pixel, and similarities of 0.94 and 0.86 for the lateral and dorsal views, respectively. These computational models are proposed as a Precision Livestock Farming tool and method for identifying and estimating postures in pigs automatically and objectively. The 2D video image repository with different pig locomotion scores can be used as a tool for teaching and research. Based on our skeleton keypoint classification results, an automatic system could be developed. This could contribute to the objective assessment of locomotion scores in sows, improving their welfare.

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Section: Methodsmentioning

confidence: 99%

“…U-Net: U-Net is a convolutional neural network (CNN) architecture with 23 layers and a "U"-shaped format 41 . The presence of both an encoder and a decoder in this architecture helps it address complex tasks such as posture classification 42 , 43 .…”

Section: Methodsmentioning

confidence: 99%

Deep learning pose detection model for sow locomotion

de Paula,

de Sousa,

Sarmiento

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…U-Net: U-Net is a convolutional neural network (CNN) architecture with 23 layers and a "U"-shaped format [31]. In training, this architecture is able to achieve accuracy on some images [32].…”

mentioning

confidence: 99%

Deep learning pose detection model for sow locomotion

Paula,

Sousa,

Sarmiento

et al. 2023

Preprint

View full text Add to dashboard Cite

Lameness affects animal mobility, causing pain and discomfort. Lameness in early stages often goes undetected due to a lack of observation, precision, and reliability. Automated and non-invasive systems offer precision and detection ease and may improve animal welfare. This study was conducted to create a repository of images and videos of sows with different locomotion scores and develop a computer vision model for automatically identifying and tracking specific points on the sow's body to facilitate lameness detection using deep learning. The video database was collected on a pig farm with a scenario built to allow filming of sows in locomotion with different lameness scores. Two stereo cameras were used to record 2D video images. Thirteen locomotion experts assessed the videos using the Locomotion Score System developed by Zinpro Corporation. From this annotated repository, computational models were trained and tested using the open-source deep learning-based animal pose tracking framework SLEAP (Social LEAP Estimates Animal Poses). The top-performing models were constructed using the LEAP architecture to accurately track 6 (lateral view) and 10 (dorsal view) skeleton keypoints. The architecture achieved average precisions values of 0.90 and 0.72, average distances of 6.83 and 11.37, and similarities of 0.94 and 0.86 for the lateral and dorsal views, respectively. These computational models are proposed as a Precision Livestock Farming tool and method for identifying and estimating postures in pigs automatically and objectively. The 2D image repository with different pig locomotion scores can be used as a tool for teaching and research. Based on our skeleton keypoint classification results, an automatic system could be developed. This could contribute to the objective assessment of locomotion scores in sows, improving their welfare.

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Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

Cited by 2 publications

References 22 publications

Deep learning pose detection model for sow locomotion

Deep learning pose detection model for sow locomotion

Deep learning pose detection model for sow locomotion

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