Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,signifi...Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.展开更多
Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional mult...Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.展开更多
Abstract： Hand drawings and two dimensional （2D） CAD drawings have been replaced by three dimensional （3D） CAD models in mechanical design, but some 2D drawings produced before are needed in the new design. Howev...Abstract： Hand drawings and two dimensional （2D） CAD drawings have been replaced by three dimensional （3D） CAD models in mechanical design, but some 2D drawings produced before are needed in the new design. However, the techniques and software packages for automatically converting 2D drawings into 3D-CAD models with high precision have not yet been developed due to the difficulties to verify the validity of the drawings, to decide the hidden lines and eoncavo-convex faces, and to represent free-form surfaces. In addition, it is very time consuming to manually convert a large number of 2D drawings into 3D CAD models. To address these problems, we propose an approach for converting 2D drawings into 3D-CAD models automatically.展开更多
基金supported by the Science and Technology Innovation Programof Hunan Province(Grant No.2022RC1021)the Hunan Provincial Natural Science Foundation Project(Grant No.2023JJ60124)+1 种基金the Changsha Natural Science Foundation Project(Grant No.kq2202265)the key project of the Hunan Provincial of Education(Grant No.22A0255).
文摘Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.
基金Supported by the National Science and Technology Major Project of China(2022ZD0119005)the Natural Science Project of Shaanxi Province(2025JC-YBMS-754,2024JC-YBMS-539)。
文摘Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.
文摘Abstract： Hand drawings and two dimensional （2D） CAD drawings have been replaced by three dimensional （3D） CAD models in mechanical design, but some 2D drawings produced before are needed in the new design. However, the techniques and software packages for automatically converting 2D drawings into 3D-CAD models with high precision have not yet been developed due to the difficulties to verify the validity of the drawings, to decide the hidden lines and eoncavo-convex faces, and to represent free-form surfaces. In addition, it is very time consuming to manually convert a large number of 2D drawings into 3D CAD models. To address these problems, we propose an approach for converting 2D drawings into 3D-CAD models automatically.
