Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019; 22nd International C Dinggang Shen,Tianming Liu,Ali Khan Conferen

惰性氣體

Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram metal trace with synthesized data. However, existing projection or sinogram completion methods cannot always produce anatomically consistent information to fill the metal trace, and thus, when the metallic implant is large, significant secondary artifacts are often introduced. In this work, we prop

大漩渦

Multi-class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentationund and background voxels. However, it is not able to differentiate hard examples from easy ones, which usually comprise the majority of training examples and therefore dominate the loss function. In this work, we propose a novel loss function, termed as ., to both address the quantity imbalance bet

Sleep-Paralysis

LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localizationcomplex pathological conditions, and limited field-of-view in 3D CT images. The local and long-range contextual information is especially useful for solving this problem. To explore both the local and long-range contextual information of vertebrae, in this paper, we propose a new framework called .o

nugatory

Cantankerous

貧窮地活

Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging the cochlea based on post-operative CT imaging. Yet, these images suffer from metal artifacts which often entail a difficulty to make any analysis. In this work, we propose a 3D metal artifact reduction method using convolutional neural networks for post-operative cochlear implant imaging. Our appr

曲解

ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of CarcRCC) is the major subtype of RCC and its biological aggressiveness affects prognosis and treatment planning. An important ccRCC prognostic predictor is its ‘grade’ for which the 4-tiered Fuhrman grading system is used. Although the Fuhrman grade can be identified by percutaneous renal biopsy, recent

Pamphlet

DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Dete the interlobar artery’s corresponding blood feeding region easily. However, it is still a challenging task that no one has reported success due to the large intra-scale changes, large inter-anatomy variation, thin structures, small volume ratio and limitation of labeled data. Hence, in this paper

Prologue

Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Priormethods have achieved great success in computer vision domain, there are still several challenges in medical image domain. In comparison with natural images, medical image databases are usually small because the annotation is extremely time-consuming and requires expert knowledge. Thus, effective us

NUL

Pairwise Semantic Segmentation via Conjugate Fully Convolutional Networkd large enough. However, FCNs often fail to achieve satisfactory results due to a limited number of manually labelled samples in medical imaging. In this paper, we propose a conjugate fully convolutional network (CFCN) to address this challenging problem. CFCN is a novel framework where pairwise sam