The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

Korsager, Anne Sofie; Fortunati, Valerio; Lijn, Fedde van der; Carl, Jesper; Niessen, Wiro J.; Østergaard, Lasse Riis; Walsum, Theo van

doi:10.1118/1.4914379

Cited by 28 publications

(14 citation statements)

References 40 publications

(75 reference statements)

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“…Accurate prostate delineation is crucial for treatment planning and effective image‐guided radiotherapy . However, manual segmentation of 3D MR images is costly, time‐consuming, and observer dependent . Prostate MR imaging artifacts, prostate gland intensity inhomogeneity, and variation in other variables such as prostate size, shape, and deformation between patients all contribute toward the challenge of automated segmentation .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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 AE 0.04, a mean surface distance (MSD) of 1.79 AE 0.46 mm, 95% Hausdorff distance (95%HD) of 7.98 AE 2.91 mm, and absolute relative volume difference (aRVD) of 15.65 AE 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 AE 0.05, MSD of 1.02 AE 0.35 mm, 95% HD of 9.50 AE 5.11 mm, and aRVD of 8.93 AE 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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…There are several automatic, and semiautomatic segmentation techniques have been investigated and developed to perform prostate MRI segmentation faster and more reproducible compared to manual contouring [7][8][9][10][11][12][13][14][15][16][17]. Korsager et al [10] presented an automatic segmentation algorithm based on atlas registration combined with intensity and shape information in a graph cut segmentation framework. Mahapatra and Buhmann [11] used a supervoxel-based image representation for segmentation of the prostate using supervoxel classification followed by a graph cut-based segmentation.…”

Section: Introductionmentioning

confidence: 99%

A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Shahedi

Halicek

et al. 2019

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling

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Segmentation of the prostate in magnetic resonance (MR) images has many applications in imageguided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts' manual reference segmentations. The measured mean ± standard deviation of error values for the whole gland were 1.4 ± 0.4 mm, 8.5 ± 2.0 mm, and 86 ± 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5

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“…The lung masks were obtained prior to this study. For three datasets (DLCST and both COPDGene datasets), the lung masks were obtained with a region-growing algorithm and postprocessing step used in [19], and for one dataset (Frederikshavn) with a method based on multi-atlas registration and graph cuts, similar to [20].…”

Section: A Notation and Feature Representationmentioning

confidence: 99%

Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease

Cheplygina

Peña

Pedersen

et al. 2018

IEEE J. Biomed. Health Inform.

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Abstract-Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd.

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The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

Abstract: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

Cited by 28 publications

References 40 publications

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

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

A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease

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