Segmentation Of Prostate Magnetic Resonance Images Research Articles

A geometric approach to robust medical image segmentation

Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances. We posit that the restricted anatomical differences between subjects could be harnessed to refine the latent space into a set of shape components. The learned set then aims to encompass the relevant anatomical shape variation found within the patient population.We explore this by utilising multiple MRI sequences to learn texture invariant and shape equivariant features which are used to construct a shape dictionary using vector quantisation. We investigate shape equivariance to a number of different types of groups. We hypothesise and prove that the greater the group order, i.e., the denser the constraint, the better becomes the model robustness. We achieve shape equivariance either with a contrastive based approach or by imposing equivariant constraints on the convolutional kernels. The resulting shape equivariant dictionary is then sampled to compose the segmentation output. Our method achieves state-of-the-art performance for the task of single domain generalisation for prostate and cardiac MRI segmentation. Code is available at https://github.com/AinkaranSanthi/A_Geometric_Perspective_For_Robust_Segmentation.

Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI

Background and Objective:Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging. Methods:This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism. Results:The results of our proposed network’s prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively. Conclusions:Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.

Segmentation of prostate region in magnetic resonance images based on improved V-Net

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.

A two-stage CNN method for MRI image segmentation of prostate with lesion

Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate cancer and other prostate diseases. The automatic segmentation of images from prostate MRI plays an important role in the auxiliary diagnosis of prostate diseases. Currently, there are two commonly used methods for automatic segmentation of prostate MRI, which are 2D image segmentation and 3D image segmentation. In this paper, a two-stage CNN method for MRI image segmentation of prostate with lesion is proposed. At the first stage, we used a CNN model incorporating the Squeeze-Excitation module to discriminate whether the image contains prostate or not. At the second stage, we proposed a Residual-Attention U-Net for segmentation of images containing prostate. Eventually, the 3D prostate MRI segmentation results are obtained and fully automated segmentation is accomplished. We evaluated our proposed method and other common 2D and 3D segmentation methods on the test dataset and compared their results based on Dice Similarity Coefficient (DSC) value. Our method performed the best and achieved the DSC metric value of 0.860.

PSP net-based automatic segmentation network model for prostate magnetic resonance imaging

Purpose: Prostate cancer is a common cancer. To improve the accuracy of early diagnosis, we propose a prostate Magnetic Resonance Imaging (MRI) segmentation model based on Pyramid Scene Parsing Network (PSP Net).Method: A total of 270 prostate MRI images were collected, and the data set was divided. Contrast limited adaptive histogram equalization (CLAHE) was enhanced in this study. We use the prostate MRI segmentation model based on PSP net, and use segmentation accuracy, under segmentation rate, over segmentation rate and receiver operating characteristic (ROC) curve evaluation index to compare the segmentation effect based on FCN and U-Net.Results: PSP net has the highest segmentation accuracy of 0.9865, over segmentation rate of 0.0023, under segmentation rate of 0.1111, which is less than FCN and U-Net. The ROC curve of PSP net is closest to the upper left corner, AUC is 0.9427, larger than FCN and U-Net.Conclusion: This paper proves through a large number of experimental results that the prostate MRI automatic segmentation network model based on PSP Net is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.

Purpose:Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep learning model to rapidly and accurately segment the prostate on MRI and to implement it as part of routine magnetic resonance-ultrasound fusion biopsy in the clinic.Materials and Methods:A total of 905 subjects underwent multiparametric MRI at 29 institutions, followed by magnetic resonance-ultrasound fusion biopsy at 1 institution. A urologic oncology expert segmented the prostate on axial T2-weighted MRI scans. We trained a deep learning model, ProGNet, on 805 cases. We retrospectively tested ProGNet on 100 independent internal and 56 external cases. We prospectively implemented ProGNet as part of the fusion biopsy procedure for 11 patients. We compared ProGNet performance to 2 deep learning networks (U-Net and holistically-nested edge detector) and radiology technicians. The Dice similarity coefficient (DSC) was used to measure overlap with expert segmentations. DSCs were compared using paired t-tests.Results:ProGNet (DSC=0.92) outperformed U-Net (DSC=0.85, p <0.0001), holistically-nested edge detector (DSC=0.80, p <0.0001), and radiology technicians (DSC=0.89, p <0.0001) in the retrospective internal test set. In the prospective cohort, ProGNet (DSC=0.93) outperformed radiology technicians (DSC=0.90, p <0.0001). ProGNet took just 35 seconds per case (vs 10 minutes for radiology technicians) to yield a clinically utilizable segmentation file.Conclusions:This is the first study to employ a deep learning model for prostate gland segmentation for targeted biopsy in routine urological clinical practice, while reporting results and releasing the code online. Prospective and retrospective evaluations revealed increased speed and accuracy.

Interactive prostate MR image segmentation based on ConvLSTMs and GGNN

Accurate segmentation of the prostate on magnetic resonance (MR) images plays an important role for prostate cancer diagnosis and treatment. Although many automated prostate segmentation methods have been proposed, the performance still faces several challenges, which includes large variability in prostate shape, unclear boundary, and complex intensity distribution. Therefore, the results obtained from the automated methods should be further refined by users to get a more accurate and reliable segmentation. In this paper, we propose an end-to-end interactive segmentation method to refine the automated results. A convolutional long short term memory (convLSTM) module and a gated graph neural network (GGNN) are presented in the proposed method for prostate segmentation in both automated and interactive manners. A boundary loss is proposed to train our model. We evaluated the proposed method on two public available datasets and one in–house dataset. Experimental results show that the proposed convLSTM module could obtain a DSC of 91.78% on the test dataset, which outperforms eight state-of-the-art methods. A further 1.5% improvements can be obtained by user interactions based on the GGNN. The segmentation time including user interactions and inference time was 2.3 min on average for segmenting one volume.

A prostate MRI segmentation method based on prior-shape-information combined with gradient and regional information

With the increasing and younger incidence of prostate cancer, the extraction of prostate contour from magnetic resonance imaging (MRI) plays an increasingly important role in clinical diagnosis and related medical research. In order to accurately segment the prostate region in MR images, we propose a level set segmentation model, which integrates the prior shape information of the prostate and the gradient information into the region-scalable fitting (RSF) model proposed by Li et al. to achieve high-precision segmentation of the prostate. In our model, we use the Gaussian probability model to establish the statistical learning of the prior shape, expressed as the external energy term of the evolution curve, and use the local grey information and the improved gradient information as the internal energy term to construct the final level set segmentation model. This method uses the information of the image itself to modify the evolution curve under the constraints of the prior shape, which can better deal with the small difference between the background and the target area in the medical image, and improve the accuracy and robustness of the segmentation. The final experimental results and analysis illustrate the feasibility of the method used in prostate MRI segmentation.

3D multi-scale discriminative network with multi-directional edge loss for prostate zonal segmentation in bi-parametric MR images

Accurate and reliable segmentation of the prostate, its inner and surrounding structures in magnetic resonance (MR) images, is of essential importance for image-guided prostate interventions and treatments. Further clinical demand is the automated segmentation of central gland (CG) and peripheral zone (PZ). Although T2-weighted (T2W) images can show prostate tissues more clearly, the lack of contrast between PZ and other surrounding tissues in T2W images increases the difficulty of PZ segmentation. In this context, it is necessary to consider using multi-parametric MR images to obtain complementary information for prostate zonal segmentation. In this paper, we propose a 3D multi-scale discriminative network with pyramid attention module (PAM) and residual refinement block (RRB) for automated and accurate segmentation of CG and PZ using bi-parametric (T2W and apparent diffusion coefficient) MR images. One of the major difficulties in prostate MR image segmentation is the ambiguous edge between the prostate and other surrounding anatomical structures, which is reflected in some specific directions. Therefore, we design a multi-directional edge loss to help the network focus on the multi-directional edge information of foreground areas. For the Prostate Multi-parametric MRI (PROMM) dataset, our proposed model achieved Dice similarity coefficient of 0.908 at CG and 0.785 at PZ. The average boundary distance obtained by our model is 1.397 mm at CG and 3.891 mm at PZ. For the NCI-ISBI dataset, our method greatly improves the Dice similarity coefficient at PZ, reaching 0.806 and achieved the Dice similarity coefficient of 0.901 at CG. The experimental results on two different MR prostate datasets demonstrate that our model is more sensitive to object boundaries and outperforms other state-of-the-art methods. The visualization of feature map activations in PAM shows that the proposed model can capture multi-scale discriminative features effectively.

Automatic segmentation of 3D prostate MR images with iterative localization refinement

Accurate segmentation of the prostate gland from Magnetic Resonance (MR) images is still a challenging problem due to large variability and heterogeneity in the prostate appearance. To overcome this problem, we present a coarse-to-fine prostate segmentation approach with iterative localization refinement. Specifically, we first propose a resolution-aware 3D U-shaped network to balance the difference between the in-plane resolution and the through-plane distance. Then a case-wise loss function is introduced to alleviate the data imbalance problem and individual differences of the prostate MR images. In the inference stage, we extract a shrunk prostate region and improve the segmentation results in an iterative manner. Evaluation experiments are carried out on the MICCAI 2012 Prostate Segmentation Challenge Dataset (PROMISE12) and the NCI-ISBI 2013 Prostate Segmentation Challenge Dataset. Comparison results demonstrate that our method achieves significant improvements over the state-of-the-art approaches, and outperforms more than 290 submissions on the website of PROMISE12.

Computer-aided diagnosis and decision-making system for medical data analysis: A case study on prostate MR images

Prostate cancer is the most common cancer in males and a major cause of cancer-related death. Magnetic resonance (MR) imaging is recently emerging as a powerful tool for prostate cancer diagnosis. To clinically diagnose prostate cancer, doctors need to segment the prostate area in the MR image. However, manual segmentation is time consuming and influenced by the physician’s experience. Computer-aided diagnosis and decision-making systems have shown great effectiveness in assisting doctors for this purpose. At the same time, deep learning based on Generative Adversarial Networks can be applied to the segmentation of prostate MR images. In this paper, we propose a new computer-aided diagnosis and decision-making system based on a deep learning model to automatically segment the prostate region from prostate MR images. Additionally, receptive field block (RFB) was integrated into the model to enhance the discriminability and robustness of the extracted multi-scale features. We also introduced dense upsampling convolution instead of the traditional bilinear interpolation to capture and recover fine-detailed information. Adversarial training was used to train the model, and the segmentation results were experimentally tested. The results showed that adversarial training and RFB are indeed effective, and the proposed method is superior to other methods on various evaluation metrics.

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.

Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.

Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation

One of the most accurate and non-invasive prostate imaging methods is magnetic resonance imaging (MRI). Segmentation is needed to find the boundary of the prostate, either automatically or semi-automatically. Recently, fully convolutional neural networks (FCNN) are being used for this purpose. In this paper, to improve the FCNN performance for prostate MRI segmentation, we analyze various structures of shortcut connections together with the size of a deep network and suggest eight different FCNNs-based deep 2D network structures for automatic MRI prostate segmentation. Our evaluations on the PROMISE12 dataset with ten-fold cross-validation indicate improved and competitive results. We analyze the results in detail, considering MRI slices, MRI volumes, test folds, and also the impact on prostate segmentation of using an EndoRectal Coil to capture the prostate MRI. Our best 2D network outperforms the state-of-the-art 3D FCNN-based methods for prostate MRI segmentation on publicly available data, without any further post-processing.

Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging

Automatic segmentation of prostate in magnetic resonance (MR) images has been more and more applied to the diagnosis of prostate disease and various clinical applications. However, due to the inhomogeneous and varying anatomical appearance around prostate boundary, the segmentation of prostate MR images faces great challenges. Since deep learning shows superior performance in computer vision, we propose a coarse-to-fine segmentation strategy by using deep neural networks to tackle the segmentation problem of the endorectal coil prostate images and non-endorectal coil prostate images separately. First, we present a registration-based coarse segmentation to the pre-processed prostate MR images to get the potential boundary region. Second, we train deep neural networks as pixel-based classifier to predict whether the pixel in the potential boundary region is prostate pixel or not. To improve the discriminability of the algorithm, we further introduce ensemble learning for fine segmentation. Finally, a boundary refinement is used to eliminate the outlier and smooth the boundary. The proposed method has been extensively evaluated on the PROMIS12 challenge dataset and PROSTATEx17 challenge dataset. Experimental results show superior segmentation performance (0.910 ± 0.036 in dice ratio, 1.583 ± 0.441 in average boundary distance and 4.579 ± 1.791 in Hausdorff distance), which demonstrates the effectiveness of the proposed algorithm.

Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. Authors overview the most recent Prostate MR image segmentation challenge results and provide insights on T2-weighted MRI scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2D prostate segmentation case. The most important benefit of this research will have medical doctors involved in the management of the cancer.

Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and computer-assisted segmentation: multiobserver study.

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

Visual saliency-based active learning for prostate magnetic resonance imaging segmentation.

We propose an active learning (AL) approach for prostate segmentation from magnetic resonance images. Our label query strategy is inspired from the principles of visual saliency that have similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low-level features. Random walks are used to identify the most informative node, which is equivalent to the label query sample in AL. To reduce computation time, a volume of interest (VOI) is identified and all subsequent analysis, such as probability map generation using semisupervised random forest classifiers and label query, is restricted to this VOI. The negative log-likelihood of the probability maps serves as the penalty cost in a second-order Markov random field cost function, which is optimized using graph cuts for prostate segmentation. Experimental results on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2012 prostate segmentation challenge show the superior performance of our approach to conventional methods using fully supervised learning.

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance imaging (MRI) scans. A statistical shape model was used as an a priori knowledge, and gray levels distribution was modeled by fitting histogram modes with a Gaussian mixture. Markov fields were used to introduce contextual information regarding voxels' neighborhoods. Final labeling optimization is based on Bayesian a posteriori classification, estimated with the iterative conditional mode algorithm. We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 12 cases, including prostates with cancer and benign prostatic hypertrophy, the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83, respectively. This new automatic prostate MRI segmentation method produces satisfactory results, even at prostate's base and apex. The method is computationally feasible and efficient.