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Utilization of convolutional neural networks for segmentation of mouse embryos cartilaginous tissue in micro-CT data
Poláková, Veronika ; Vičar, Tomáš (oponent) ; Chmelík, Jiří (vedoucí práce)
Automatic segmentation of the biological structures in micro-CT data is still a challenge since the object of interest (craniofacial cartilage in our case) is commonly not characterized by unique voxel intensity or sharp borders. In recent years, convolutional neural networks (CNNs) have become exceedingly popular in many areas of computer vision. Specifically, for biomedical image segmentation problems, U-Net architecture is widely used. However, in the case of micro-CT data, there is a question whether 3D CNN would not be more beneficial. The master thesis introduced CNN architecture based on V-Net as well as the methodology for data preprocessing and postprocessing. The baseline architecture was further optimized using advanced architectural modifications such as Atrous Spatial Pyramid Pooling (ASPP) module, Scaled Exponential Linear Unit (SELU) activation function, multi-output supervision and Dense blocks. For network learning, modern approaches were used including learning rate warmup or AdamW optimizer. Even though the 3D CNN do not outperform U-Net regarding the craniofacial cartilage segmentation, the optimization raises the median of Dice coefficient from 69.74 % to 80.01 %. Therefore, utilizing these advanced architectural modifications is highly encouraged as they can be easily added to any U-Net-like architecture and may remarkably improve the results.
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Utilization Of Convolutional Neural Networks For Segmentation Of Mouse Embryos Cartilaginous Tissue In Micro-Ct Data
Poláková, Veronika
Automatic segmentation of the biological structures in micro-CT data is still a challengesince the object of interest (craniofacial cartilage in our case) is commonly not characterized by uniquevoxel intensity or sharp borders. In recent years, convolutional neural networks (CNNs) have becomeexceedingly popular in many areas of computer vision. Specifically, for biomedical image segmentationproblems, U-Net architecture is widely used. However, in case of micro-CT data, there isa question whether 3D CNN would not be more beneficial. This paper introduces CNN architecturebased on V-Net as well as the methodology for data preprocessing and postprocessing. The baselinearchitecture was further optimized using advanced techniques such as Atrous Spatial Pyramid Pooling(ASPP) module, Scaled Exponential Linear Unit (SELU) activation function, multi-output supervisionand Dense blocks. For network learning, modern approaches were used including learning ratewarmup or AdamW optimizer. Even though the 3D CNN do not outperform U-Net regarding the craniofacialcartilage segmentation, the optimization raises the median of Dice coefficient from 69.74 %to 80.01 %. Therefore, utilizing these advanced techniques is highly encouraged as they can be easilyadded to any U-Net-like architecture and may remarkably improve the results.
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Utilization of convolutional neural networks for segmentation of mouse embryos cartilaginous tissue in micro-CT data
Poláková, Veronika ; Vičar, Tomáš (oponent) ; Chmelík, Jiří (vedoucí práce)
Automatic segmentation of the biological structures in micro-CT data is still a challenge since the object of interest (craniofacial cartilage in our case) is commonly not characterized by unique voxel intensity or sharp borders. In recent years, convolutional neural networks (CNNs) have become exceedingly popular in many areas of computer vision. Specifically, for biomedical image segmentation problems, U-Net architecture is widely used. However, in the case of micro-CT data, there is a question whether 3D CNN would not be more beneficial. The master thesis introduced CNN architecture based on V-Net as well as the methodology for data preprocessing and postprocessing. The baseline architecture was further optimized using advanced architectural modifications such as Atrous Spatial Pyramid Pooling (ASPP) module, Scaled Exponential Linear Unit (SELU) activation function, multi-output supervision and Dense blocks. For network learning, modern approaches were used including learning rate warmup or AdamW optimizer. Even though the 3D CNN do not outperform U-Net regarding the craniofacial cartilage segmentation, the optimization raises the median of Dice coefficient from 69.74 % to 80.01 %. Therefore, utilizing these advanced architectural modifications is highly encouraged as they can be easily added to any U-Net-like architecture and may remarkably improve the results.
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