Prostate segmentation in MR images using discriminant boundary features

Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network

BACKGROUND AND OBJECTIVE

Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technology, magnetic resonance images (MRI) have been used in the diagnosis and treatment of prostate cancer because of its clarity and non-invasiveness. Prostate MRI segmentation and diagnosis experience problems such as low tissue boundary contrast. The traditional segmentation method of manually drawing the contour boundary of the tissue cannot meet the clinical real-time requirements. How to quickly and accurately segment the prostate tumor has become an important research topic.

METHODS

This paper proposes a prostate tumor diagnosis based on the deep learning network PSP-Net+VGG16. The deep convolutional neural network segmentation method based on the PSP-Net constructs a atrous convolution residual structure model extraction network. First, the three-dimensional prostate MRI is converted to two-dimensional image slices, and then the slice input of the two-dimensional image is trained based on the PSP-Net neural network; and the VGG16 network is used to analyze the region of interest and classify prostate cancer and normal prostate.

RESULTS

According to the experimental results, the segmentation method based on the deep learning network PSP-Net is used to identify the data set samples. The segmentation accuracy is close to the Dice similarity coefficient and Hausdorff distance, and even exceeds the traditional prostate image segmentation method. The Dice index reached 91.3%, and the technique is superior in speed of processing. The predicted tumor markers are very close to the actual markers manually by clinicians; the classification accuracy and recognition rates of prostate MRI based on VGG16 are as high as 87.95% and 87.33%, and the accuracy rate and recall rate of the network model are relatively balanced. The area under curve index is also higher than other models, with good generalization ability.

CONCLUSION

Experiments show that prostate cancer diagnosis based on the deep learning network PSP-Net+VGG16 is superior in accuracy and processing time compared to other algorithms, and can be well applied to clinical prostate tumor diagnosis.

Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy

Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary coding representation learning. Two sub-networks under the supervision of the dilation and erosion masks transformed from the manually delineated organ mask are first separately trained to learn the spatial-semantic context near the organ boundary. Then we encode the organ boundary based on the predictions of these two sub-networks and design a multi-atlas based refinement strategy by transferring the knowledge from training data to inference. 2) Organ segmentation. The boundary coding representation as context information, in addition to the image patches, are used to train the final segmentation network. Experimental results on a large and diverse male pelvic CT dataset show that our method achieves superior performance compared with several state-of-the-art methods.

Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI

In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.

Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation

Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small effective area of the prostate MR images. In order to solve these problems, we propose a novel end-to-end professional network which consists of an Encoder-Decoder structure with dense dilated spatial pyramid pooling (DDSPP) for prostate segmentation based on deep learning. First, the DDSPP module is used to extract the multi-scale convolution features in the prostate MR images, and then the decoder is used to capture the clear boundary of prostate. Competitive results are produced over state of the art on 130 MR images which key metrics Dice similarity coefficient (DSC) and Hausdorff distance (HD) are 0.954 and 1.752 mm respectively. Experimental results show that our method has high accuracy and robustness.

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

PURPOSE

Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other problems. Manual segmentation can be time-consuming and is subject to inter- and intraobserver variation. We developed an automated deep learning-based method to address this technical challenge.

METHODS

We propose a three-dimensional (3D) fully convolutional networks (FCN) with deep supervision and group dilated convolution to segment the prostate on magnetic resonance imaging (MRI). In this method, a deeply supervised mechanism was introduced into a 3D FCN to effectively alleviate the common exploding or vanishing gradients problems in training deep models, which forces the update process of the hidden layer filters to favor highly discriminative features. A group dilated convolution which aggregates multiscale contextual information for dense prediction was proposed to enlarge the effective receptive field of convolutional neural networks, which improve the prediction accuracy of prostate boundary. In addition, we introduced a combined loss function including cosine and cross entropy, which measures similarity and dissimilarity between segmented and manual contours, to further improve the segmentation accuracy. Prostate volumes manually segmented by experienced physicians were used as a gold standard against which our segmentation accuracy was measured.

RESULTS

The proposed method was evaluated on an internal dataset comprising 40 T2-weighted prostate MR volumes. Our method achieved a Dice similarity coefficient (DSC) of 0.86 ± 0.04, a mean surface distance (MSD) of 1.79 ± 0.46 mm, 95% Hausdorff distance (95%HD) of 7.98 ± 2.91 mm, and absolute relative volume difference (aRVD) of 15.65 ± 10.82. A public dataset (PROMISE12) including 50 T2-weighted prostate MR volumes was also employed to evaluate our approach. Our method yielded a DSC of 0.88 ± 0.05, MSD of 1.02 ± 0.35 mm, 95% HD of 9.50 ± 5.11 mm, and aRVD of 8.93 ± 7.56.

CONCLUSION

We developed a novel deeply supervised deep learning-based approach with a group dilated convolution to automatically segment the MRI prostate, demonstrated its clinical feasibility, and validated its accuracy against manual segmentation. The proposed technique could be a useful tool for image-guided interventions in prostate cancer.

SEMI-SUPERVISED LEARNING FOR PELVIC MR IMAGE SEGMENTATION BASED ON MULTI-TASK RESIDUAL FULLY CONVOLUTIONAL NETWORKS

Accurate segmentation of pelvic organs from magnetic resonance (MR) images plays an important role in image-guided radiotherapy. However, it is a challenging task due to inconsistent organ appearances and large shape variations. Fully convolutional network (FCN) has recently achieved state-of-the-art performance in medical image segmentation, but it requires a large amount of labeled data for training, which is usually difficult to obtain in real situation. To address these challenges, we propose a deep learning based semi-supervised learning framework. Specifically, we first train an initial multi-task residual fully convolutional network (FCN) based on a limited number of labeled MRI data. Based on the initially trained FCN, those unlabeled new data can be automatically segmented and some reasonable segmentations (after manual/automatic checking) can be included into the training data to fine-tune the network. This step can be repeated to progressively improve the training of our network, until no reasonable segmentations of new data can be included. Experimental results demonstrate the effectiveness of our proposed progressive semi-supervised learning fashion as well as its advantage in terms of accuracy.

Molecular imaging and fusion targeted biopsy of the prostate

PURPOSE

This paper provides a review on molecular imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI) for prostate cancer detection and its applications in fusion targeted biopsy of the prostate.

METHODS

Literature search was performed through the PubMed database using the keywords "prostate cancer", "MRI/ultrasound fusion", "molecular imaging", and "targeted biopsy". Estimates in autopsy studies indicate that 50% of men older than 50 years of age have prostate cancer. Systematic transrectal ultrasound (TRUS) guided prostate biopsy is considered the standard method for prostate cancer detection and has a significant sampling error and a low sensitivity. Molecular imaging technology and new biopsy approaches are emerging to improve the detection of prostate cancer.

RESULTS

Molecular imaging with PET and MRI shows promising results in the early detection of prostate cancer. MRI/TRUS fusion targeted biopsy has become a new clinical standard for the diagnosis of prostate cancer. PET molecular image-directed, three-dimensional ultrasound-guided biopsy is a new technology that has great potential for improving prostate cancer detection rate and for distinguishing aggressive prostate cancer from indolent disease.

CONCLUSION

Molecular imaging and fusion targeted biopsy are active research areas in prostate cancer research.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods.

An efficient MRF embedded level set method for image segmentation

This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 × 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory.

Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features

PURPOSE

To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input.

MATERIALS AND METHODS

Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors.

RESULTS

In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence.

CONCLUSION

Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input.