Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes

Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease. In this paper, we describe a two-stage framework for kidney and tumor segmentation based on 3D fully convolutional network (FCN). The first stage preliminarily locate the kidney and cut off the irrelevant background to reduce class imbalance and computation cost. Then the second stage precisely segment the kidney and tumor on the cropped patch. The proposed method achieves 98.05% and 83.70% of Dice score on the validation set of MICCAI 2019 KiTS Challenge

A double cascaded framework based on 3D SEAU-Net for kidney and kidney tumor Segmentation

Accurate segmentation of kidney and kidney tumor from CT-volumes is vital to many clinical endpoints, such as differential diagnosis, prognosis and radiation therapy planning. While manual segmentation is subjective and time-consuming, fully automated extraction is quite imperative and challenging due to intrinsic heterogeneity of tumor structures. To address this problem, we propose a double cascaded framework based on 3D SEAU-Net to hierarchically and successively segment the subregions of the target. This double cascaded framework is used to decompose the complex task of multi-class segmentation into two simpler binary segmentation tasks. That is to say, the region of interest (ROI) including kidney and kidney tumor is trained and extracted in the first step, and the pre-trained weights are used as the initial weights of the network that is to segment the kidney tumor in second step. Our proposed network, 3D SEAU-Net, integrates residual network, dilated convolution, squeeze-and-excitation network and attention mechanism to improve segmentation performance. To speed training and improve network generalization, we take advantage of transfer learning (i.e., weight transfer) in the whole training phase. Meanwhile, we use 3D fully connected conditional random field to refine the result in post-processing phase. Eventually, our proposed segmentation method is evaluated on KiTS 2019 dataset and experimental results achieves mean dice scores 93.51% for the whole kidney and tumor, 92.42% for kidney and 74.34% for tumor on the training data

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201