Kidney segmentation plays an important role in the diagnosis of kidney diseases.The volume and thickness of renal cortex are effective assessment criteria in early clinical diagnosis for renal neoplasms

chronic arteriosclerotic nephropathy

and acute rejection after kidney transplant.However

most existing methods focus on the whole kidney segmentation.This paper presents a fully automatic renal cortex segmentation based on fully convolutional network and GrowCut for multi-modality kidney images. Generalized Hough Transform(GHT) is used to detect non-analytic shape represented by R-table.GHT localizes the kidney and then the region of interest(ROI) is extracted.The fully convolutional network(FCN-32s) for semantic segmentation is introduced into renal cortex segmentation.Data augmentation is employed to expand labeled data and transfer learning is applied due to lack of sufficient training data.The initial parameters of the proposed network are taken from pre-trained model of VGG-16.All the fully connected layers of VGG-16 are converted into convolutional layers.The filter size of fc8 is changed from 1 000 to 2 because the proposed network regards cortex and background as two classes.The filter sizes of fc6 and fc7 are modified from 4 096 into 1024 to reduce parameters.Pooling and down-sampling layers can extract more abstract features for image classification tasks.For image segmentation

however

too much trivial information will be lost.The proposed network retains the first three pooling layers of VGG-16.Dropout

as a regularization method

prevents over-fitting

and loss function is optimized with Stochastic Gradient Descent.The proposed network is a first implementation for realizing renal cortex segmentation based on the fully convolutional network.However

the obvious disadvantage of fully convolutional network is that it is a pixel-wise classification and does not consider the spatial relationship among pixels.It will cause some unexpected results.The segmentation regions have burrs at the edge

and cortex of some slices is missegmented.The proposed method combines fully convolutional network and GrowCut for superior cortex segmentation.GrowCut is an interactive segmentation method based on the labeled seeds

and the pixels compete to gain labels in the neighborhood.The performance of GrowCut relies on the initial seeds marked by the user.In this paper

the seeds are generated by the proposed network

which realizes an automatic implementation and frees manual interventions.The images of test set are firstly segmented by the proposed network

and the feature maps of the last convolutional layer are extracted as a labeled map.The mislabeled seeds that always appears in spine

spleen

and other adjacent tissues can be corrected by GrowCut with priors of kidney.GrowCut can achieve more accurate cortex segmentation based on the correctly labeled map.A set of contrast-enhanced CT images and corresponding ground truth labeled by experts is given for training fully convolutional network.The ROI will be normalized after it is extracted from the original image

and then expanded after cutting and reflections.These images are split into a training set(5 000) and a validation set(300).Parameters of the network will be adjusted in the training processing.The proposed network is trained on a deep learning framework Caffe

with maximum iteration of 10 000

initial learning rate of 0.001

which is multiplied by 0.1 at every 2 000 interactions

momentum of 0.9

weight delay of 0.000 5

and batch size of 1 for online learning.The proposed network is quantitatively compared with three pre-trained fully convolutional networks by three metrics:pixel accuracy

overall accuracy

and mean region intersection over union(IU).The proposed method is compared with four methods

including two fully convolutional networks and two typical methods.Two metrics are used

namely

IU and Dice Similarity Coefficient(DSC)

which is a common metric in medical image segmentation. The experimental dataset contains 30 clinical CT and MRI images.The proposed method achieved IU of 91.06%±2.34% and DSC of 91.79%±2.39%.The proposed fully convolutional network has fewer parameters and higher accuracy than basic fully convolutional network for renal cortex segmentation.GrowCut algorithm considers the neighborhood information between pixels

thus further improving the segmentation accuracy by 3%.Results show that contrast-enhanced and non-contrast-enhanced images can be accurately segmented. Deep learning model trained with huge data set of natural images can extract hierarchical features and can be introduced in medical images segmentation by transfer learning.Missegmentation is reduced by the proposed network and can be corrected effectively.The experimental results indicate that the proposed method is more suitable for kidney images from different modalities and outperforms typical methods.The proposed method can provide a reliable basis for clinical diagnosis because medical images from normal and abnormal kidneys both can be segmented accurately.The furture work includes the following tasks:the accuracy of non-contrast-enhanced image segmentation will be further improved by optimizing algorithm;the proposed network will be used for segmentation of other organs by fine-tuning the parameters of network.GrowCut will be plugged in the network as a new layer to perform end-to-end training.