Prostate cancer is one of the leading causes of deaths due to cancer among older men

and its diagnosis experiences many challenging problems. Imaging-based prostate cancer screening

such as magnetic resonance imaging (MRI)

requires an experienced medical professional to extensively review the obtained data and perform a diagnosis. The first step in prostate radiation therapy is to identify the difference between the original image and the nearby prostate tissue. However

prostate MRI results face the problems of low organizational boundary contrast ratio and lack of effective areas. Manual segmentation will take considerable time

which cannot meet clinical real-time requirements. Although several methods presented in the MICCAI 2012 challenge achieved reasonable results

they highly depended on feature selection or statistical shape modeling performance

and thus

presented limited success. A segmentation algorithm for prostate MRI based on a deep deconvolutional neural network is proposed to solve the aforementioned deficiencies. Inspired by the latest deep learning technology

fully convolutional network

and DeconvNet

we present a multi-layer deconvolutional convolutional network to demonstrate that a deep neural network can dramatically increase the automated segmentation of prostate MRI images compared with systems based on handcrafted features. The deep neural network model exhibits strong feature learning and end-to-end training capacities

which provide better performance than former image processing techniques. Unlike an image classification task

each pixel in an MRI image is regarded as an object that should be classified. Hence

we obtain the final segmentation results by considering the prediction of prostate tissues as a two-stage classification task. This study presents a multi-layer convolutional network

which utilizes a convolution filter

a pooling layer

and a decoder network to transform an input MRI image into a probability map. A convolutional neural network is used in the training process of this model to extract highly distinct image features. Then

a deconvolution strategy is adopted to expand the feature map size and to maintain the sizes of the input image and the output probability map. The stacked convolution and deconvolution layers can maintain resolution size by adding a pad to the input image. In addition to achieving deeper network architecture

the stacked convolution layers exhibit strong robustness against overfitting. Finally

the probability map is used to train a softmax classifier and the final segmentation result is obtained. We replace the classical neuron activation function with a rectified linear unit in our model to speed up the training process and avoid the vanishing gradient. The Dice similarity coefficient is used as the loss function in our convolutional network to overcome the problem of low effective organization in the original image. The images provided in MICCAI 2012 exhibit varying sizes and resolutions

and thus

we preprocess the images and augment the size of the data set via multi-scale cropping and scale transformation to improve training reliability. All the experiments are performed on the MICCAI 2012 data set. The algorithm proposed in this study uses the Dice similarity coefficient and Hausdorff distance as evaluation metrics. The Dice similarity coefficient is over 89.75%

whereas Hausdorff distance is shorter than 1.3 mm

which can realize the segmentation accuracy of traditional methods. Furthermore

the processing time is shortened to within 1 min

which is clearly superior to those of other methods. The deep learning approach is gradually being applied to the medical field. This study introduces a new deep learning method that is used to segment prostate images. Both the qualitative and quantitative experiments show that the prostate segmenting method based on the deconvolutional neural network can segment MRI images accurately. The proposed method can attain higher segmentation accuracy than the traditional methods. All the calculations are performed on a graphics processing unit

and handling time is considerably shortened compared with those of other segmentation algorithms. Therefore

the proposed model is highly appropriate for the clinical segmentation of prostate images.