2018/03/14 · Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In ...

This enables accurate and automatic multi-organ organ segmentation from 3D radiological scans, important yet challenging problem for medical image analysis. •.

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images.

We showed that a cascaded deployment of volumetric fully convolutional networks (3D U-Net) can produce competitive results for medical image segmentation.

2018/03/20 · In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large.

This paper investigates fully convolutional neural networks (FCNs) and proposes a modified 3D U-Net architecture devoted to the processing of computed ...

2018/03/21 · Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images.

TL;DR: This work shows that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures can achieve competitive segmentation ...

3D全卷积网络（FCN）的最新进展使产生体积图像的密集体素预测变得可行。在这项工作中，我们表明，通过对几种解剖结构（从大器官到细血管）进行手动标记的CT扫描训练的多类3D FCN， ...

An application of cascaded 3D fully convolutional networks for medical image segmentation HR Roth, H Oda, X Zhou, N Shimizu, Y Yang, Y Hayashi, M Oda