ترغب بنشر مسار تعليمي؟ اضغط هنا

Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network

134   0   0.0 ( 0 )
 نشر من قبل Nikolas Lessmann
 تاريخ النشر 2019
  مجال البحث هندسة إلكترونية
والبحث باللغة English




اسأل ChatGPT حول البحث

Thin-plate splines can be used for interpolation of image values, but can also be used to represent a smooth surface, such as the boundary between two structures. We present a method for partitioning vertebra segmentation masks into two substructures, the vertebral body and the posterior elements, using a convolutional neural network that predicts the boundary between the two structures. This boundary is modeled as a thin-plate spline surface defined by a set of control points predicted by the network. The neural network is trained using the reconstruction error of a convolutional autoencoder to enable the use of unpaired data.



قيم البحث

اقرأ أيضاً

340 - J. Rivet , A. Taliercio , C. Fang 2020
Digital hologram rendering can be performed by a convolutional neural network, trained with image pairs calculated by numerical wave propagation from sparse generating images. 512-by-512 pixeldigital Gabor magnitude holograms are successfully estimat ed from experimental interferograms by a standard UNet trained with 50,000 synthetic image pairs over 70 epochs.
Perception plays an important role in reliable decision-making for autonomous vehicles. Over the last ten years, huge advances have been made in the field of perception. However, perception in extreme weather conditions is still a difficult problem, especially in rainy weather conditions. In order to improve the detection effect of road targets in rainy environments, we analyze the physical characteristics of the rain layer and propose a deraining convolutional neural network structure. Based on this network structure, we design an ablation experiment and experiment results show that our method can effectively improve the accuracy of object detection in rainy conditions.
Breast cancer has become one of the most prevalent cancers by which people all over the world are affected and is posed serious threats to human beings, in a particular woman. In order to provide effective treatment or prevention of this cancer, dise ase diagnosis in the early stages would be of high importance. There have been various methods to detect this disorder in which using images have to play a dominant role. Deep learning has been recently adopted widely in different areas of science, especially medicine. In breast cancer detection problems, some diverse deep learning techniques have been developed on different datasets and resulted in good accuracy. In this article, we aimed to present a deep neural network model to classify histopathological images from the Databiox image dataset as the first application on this image database. Our proposed model named BCNet has taken advantage of the transfer learning approach in which VGG16 is selected from available pertained models as a feature extractor. Furthermore, to address the problem of insufficient data, we employed the data augmentation technique to expand the input dataset. All implementations in this research, ranging from pre-processing actions to depicting the diagram of the model architecture, have been carried out using tf.keras API. As a consequence of the proposed model execution, the significant validation accuracy of 88% and evaluation accuracy of 72% obtained.
Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
106 - Yuyang Xue , Jiannan Su 2019
The traditional image compressors, e.g., BPG and H.266, have achieved great image and video compression quality. Recently, Convolutional Neural Network has been used widely in image compression. We proposed an attention-based convolutional neural net work for low bit-rate compression to post-process the output of traditional image compression decoder. Across the experimental results on validation sets, the post-processing module trained by MAE and MS-SSIM losses yields the highest PSNR of 32.10 on average at the bit-rate of 0.15.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا