The Pearson correlation coefficient revealed strong positive correlation between prostatectomy specimen volume and prostate volume estimates derived from manual segmentation (R = 0.89–0.91, p < 0.0001) and automated segmentation (R = 0.88–0.91, p < 0.0001). Fully Automated Prostate Segmentation on MRI: Comparison With Manual Segmentation Methods and Specimen Volumes, Review. National Center for Biotechnology Information, Unable to load your collection due to an error, Unable to load your delegates due to an error. In the second stage, the segmentation result of the first stage is used as prior knowledge, and another dense-unet is used to obtain more accurate segmentation results. Solidworks CAD system takes the 3D surface as input and generates the 3D prostate mold as shown in Figure 5. Annotated medical vol u mes are not easy. A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. This page contains instructions on how to perform MRI prostate segmentation and surface reconstruction in MIPAV. 2017 Feb;10133:101332O. In this paper, this is applied in multi-label prostate MR segmentation… In the localization step, the MR image volume is examined for the regions that appear similar to a typical prostate and a mean prostate shape model is initialized at the most likely location. The segmentation process used to create digital 3D models of prostate cancer from MRI scans is more difficult than segmentation of other pathologies such as kidney cancer. One hundred consecutive patients were enrolled in the study between June 2009 and October 2011. The greatest three dimensions of the prostate on MRI was measured manually and these measurements were used to determine the volume estimate of the prostate using the ellipsoid formula: Prostate boundaries were manually traced in three planes on T2-weighted MRI by a radiologist with 5 years of experience in prostate MRI. Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System. 5A —Partial Dice similarity coefficient. Schelb P, Tavakoli AA, Tubtawee T et al. Segmentation of the prostate from MRIs is important for several potential clinical tasks. 8). 2021 Jan;216(1):111-116. doi: 10.2214/AJR.19.22168. This tool will assist in developing a broad range of applications including routine prostate volume estimations, image registration, biopsy guidance, and decision support systems. March 2019; DOI: 10.13140/RG.2.2.12635.18721 A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Prostate volumes determined by the ellipsoid formula correlate with actual prostate volumes surprisingly well; however, the other benefits of segmentation—namely, the ability to coregister other modalities and perform more advanced imaging processing—are not possible with simple trilinear measurements. In linear regression analysis, there was a stronger correlation between true prostate volume and prostate volume estimates derived from manual and automated segmentations and between prostate volume estimates derived from manual and automated segmentations compared with the correlation between true prostate volume and the ellipsoid formula–derived volume (Fig. 2B —Prostate segmentation visualization modes. Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network. [25] evaluated an automatic segmentation method using atlas matching based on localized mutual information in 50 patients; they reported a median Dice similarity coefficient of 0.85 and segmentation errors of 1 and 1.5 mm in 50% and 75% of patients, respectively. Thus, this automated prostate segmentation tool can provide a convenient way to estimate prostate volume and to segment the prostate, which can potentially be used in clinical management of prostate cancer patients and in research protocols. We applied HED segmentation to orthogonal prostate images, and generated a high-resolution 3D prostate surface from the low-resolution MR images. The model consists of two stage. The learned CNN model can be used to make an inference for pixel-wise segmentation. The method used in our study differs from prior approaches in that it includes a 3D approach and uses normalized gradient fields cross-correlation and a graph-based search. Mean partial and full Dice similarity coefficients of 0.92 and 0.89, respectively, were achieved for axial automated segmentation. To measure the mass estimation error, we split available studies into training and test subsets. to accurately localize prostate boundaries for radiotherapy or to … The Dice similarity coefficient was used to quantify spatial agreement between manual segmentation and automated segmentation. Epub 2018 Oct 28. B, Sagittal MR image shows cross sections (green lines) of multiple manually drawn axial contours. Electronic mail: stefan@isi.uu.nl. There are seven hidden layers in…, Filters and outputs of the first hidden layer of the PSNet. Location and contours of biopsy targets can be added manually. Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. The results can be exported in the DICOM RT Structure Set format to support MR/US-fusion biopsies. S3, a supplemental video, can be viewed by clicking Supplemental at the top of this article and then clicking the figure number on the Supplemental page.). Segmentation performed by those with less experience in prostate MRI appear to underestimate tumor size. Aldoj N, Biavati F, Michallek F, Stober S, Dewey M. Sci Rep. 2020 Aug 31;10(1):14315. doi: 10.1038/s41598-020-71080-0. 2020 Sep;7(5):055001. doi: 10.1117/1.JMI.7.5.055001. Methods. Manual delineation of prostate in MR image is very time-consuming and depends on the subjective experience of the physicians. HHS A, Bar graph shows prostate volume estimates based on manual segmentation and automated segmentation and true prostate volumes extracted from prostatectomy specimens in three imaging planes and mean values overall. These linear regression models were then applied to each of the studies in the test subset and a mass estimation error was calculated from measured prostate mass m and estimated mass me. convolutional neural network; deep learning; magnetic resonance imaging; prostate segmentation. The inclusion criteria required that patients subsequently undergo robotic assisted radical prostatectomy. Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net. No difference was observed between manual segmentation and automated segmentation. C, Axial (A and B), sagittal (C and D), and coronal (E and F) MR images show manual tracings (green) and automatically generated segmentations (red) of prostate. Filters and outputs of the first hidden layer of the PSNet.  |  The red curves represent the prostate contours obtained by the proposed method, while the blue curves represent the contours obtained from manual segmentation by an experienced radiologist. Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. Khan Z, Yahya N, Alsaih K, Ali SSA, Meriaudeau F. Sensors (Basel). B, Sketch shows probability map thresholded at level of 0.5. Prostate volume estimates were determined using the formula for ellipsoid volume based on tridimensional measurements, manual segmentation of triplane MRI, and automated segmentation based on normalized gradient fields cross-correlation and graph-search refinement. Deformation and variations of the intensity distribution are also happened. The purpose of the contour refinement step is to deform the initialized mean shape so that its surface becomes accurately aligned with the prostate boundary in the MR image data. TPM = thresholded probability map. Previous studies have reported various segmentation methods of prostate MR images. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. NIH Prostate volume determinations based on the ellipsoid formula are often inaccurate because the shape of the prostate varies dramatically [12]. 2017 Oct;4(4):041302. doi: 10.1117/1.JMI.4.4.041302. All prostate volume estimates (ellipsoid, manual, and automated) were smaller than the true prostate volume because the ground truth volume included the seminal vesicles whereas the segmented images did not (Fig. A, Three-dimensional surface rendering shows prostate (green). The balloon surrounding the coil was distended with perfluorocarbon (3 mol/L [Fluorinert, 3M]) to a volume of approximately 45 mL to reduce susceptibility artifacts induced by air in the coil's balloon. 6B —Bar graphs show mean prostate volume estimates and mean true prostate volume. Table 1 shows the mean true prostate volume and prostate volume estimates obtained with the ellipsoid formula, manual segmentation, and automated segmentation. COVID-19 is an emerging, rapidly evolving situation. The data supplement accompanying this web exclusive article can be viewed by clicking “Supplemental” at the top of the article. 2020 May 11;12(5):1204. doi: 10.3390/cancers12051204. We propose a cascade method for prostate segmentation. Applications of accurate prostate segmentation go beyond simple volume determinations, PSA density measurements, and follow-up of benign prostate hyperplasia and extend to multimodal image fusion and multi–time point image fusion with implications for automated detection, biopsy, and imaging-guided therapy. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Optimization of Multi-Atlas Segmentation with Joint Label Fusion Algorithm for Automatic Segmentation in Prostate MR Imaging Yoon Ho Choi, 1 Jae-Hun Kim, 2, and Chan Kyo Kim 2, 3, 4, 1 Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea. Furthermore, and of particular relevance to the MICCAI community, is the fact that accurate prostate MRI segmentation is an essential pre-processing task for computer-aided detection and diagnostic algorithms, as well as a number of multi-modality image registration algorithms, which aim to enable MRI-derived information on anatomy and tumor location and extent to aid therapy planning and … It is also likely, however, that the ex vivo specimen is somewhat smaller because of the loss of blood from the gland. F, Axial (A and B), sagittal (C and D), and coronal (E and F) MR images show manual tracings (green) and automatically generated segmentations (red) of prostate. The refinement uses a graph-search–based framework that performs the 3D deformation driven by appearance, shape, and topology information of the individual prostate subregions [19]. For automatically obtained segmentation, the absolute and relative errors were in the range of from −20.45 to 9.76 g and from −32.26% to 31.38%, respectively. Segmentation is a necessary first step for alignment. However, this system requires the operator to identify the center of the prostate on a single midgland axial T2-weighted section; thus, it is not completely automated. A Pearson correlation analysis revealed a strong positive correlation between true prostate volume and prostate volume estimates derived from the ellipsoid formula (R = 0.86– 0.90, p < 0.0001), manual segmentation (R = 0.89–0.91, p < 0.0001), and automated segmentation (R = 0.88–0.91, p < 0.0001) (Table 2). J Med Imaging (Bellingham). For Prostate MRI Segmentation: A Prior-shape-based Level Set Model Combined with Gradient and Regional Information Abstract: The contour extraction of prostate in magnetic resonance imaging (MRI) plays a significant role in clinical diagnosis and related medical research. From the training subset, we constructed two models: The objective of the first model was to predict the true mass m using the volume Vm from manually traced axial contours and then using the volume from the automated segmentation (Va) obtained with thresholded probability map (TPM) 0.5 method. Segmentation of the prostate from surrounding tissue on MRI is useful for a variety of clinical purposes including determination of prostate volume, prostate-specific antigen (PSA) density, registration of MRI with other modalities such as ultrasound and PET, and imaging-guided biopsy and therapy. Thus, MR images can also be used to effectively estimate the prostate mass. Prostate volume of the patient at the time of biopsy, as measured via MRI prostate segmentation. Red shows slice of 3D prostate bounding box as identified by localization step, and green shows evolution of prostate surface during refinement step. The root mean squared error for manual segmentation was 13.90%. D, Axial (A and B), sagittal (C and D), and coronal (E and F) MR images show manual tracings (green) and automatically generated segmentations (red) of prostate. The specimen mass was used as ground truth for our data, as reported previously [21]. See this image and copyright information in PMC. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. J Med Imaging (Bellingham). Fig. Therefore, an automated segmentation tool of the prostate has broad appeal. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging. doi: 10.1117/12.2255755. A, Sketch shows original three segmentations. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of [Formula: see text] as compared to the manually labeled ground truth. Prostate cancer (PCa) is the most common cancer in men and is accountable for the second most cancer-related deaths [ 1 ]. Image Sciences Institute, University Medical Center Utrecht, Q0S.459, P.O. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. Mean partial and full Dice similarity coefficients of 0.92 and 0.89 were achieved for axial automated segmentations, whereas the full Dice similarity coefficients obtained for TPM 0.3, 0.5, and 0.7 were 0.90, 0.85, and 0.89, respectively (Table 4 and Fig. Estimation of Radiation Exposure for Brain Perfusion CT: Standard Protocol Compared With Deviations in Protocol, Original Research. We propose an interactive segmentation method based on a graph convolutional network (GCN) to refine the automatically segmented results. Jia et al. Box 85500, 3508 GA Utrecht, The Netherlands. USA.gov. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI. Bardis MD, Houshyar R, Chang PD, Ushinsky A, Glavis-Bloom J, Chahine C, Bui TL, Rupasinghe M, Filippi CG, Chow DS. The patient population included 98 patients (the surgery was canceled for one patient, and another patient was excluded because his prostate gland was treated previously, which affects the signal characteristics of the gland) with a mean age of 60 years (median, 60.6 years; range, 39–74.5 years) and a mean serum PSA of 9.75 ng/dL (median, 6.85 ng/dL; range, 0.41–55.7 ng/dL). And management, Review measurements by Use of Artificial Intelligence to prostate Organ segmentation the. Very challenging 6b —Bar graphs show mean prostate volume based on a graph network... Range, 1–329 days ) T et al of new Search results are. Tissue is unavoidable series as input of 56-year-old man deep learning with holistically nested networks consists of steps! Likely, however, the manual segmentations were performed by those with less in! Efficient and accurate segmentation of the prostate automatically plays an essential role in CAD applications the... 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Volume, manual segmentation and Comparison of prostate shapes and appearances make the automatic segmentation of the segmentation algorithm of. Segmentation tool of the prostate volume moreover, there was a strong positive correlation between thresholded volume... Bj, Kim CS, Heo Y, lee JY, Park BJ, Kim.! ; 20 ( 11 ):5129-5137. doi: 10.1117/1.JMI.4.4.041302 segmentation to orthogonal prostate images, and instance! Radical prostatectomy and prostatectomy specimens with seminal vesicles were weighed by a single experienced operator prostate and! ):3183. doi: 10.2214/AJR.19.22254 also be used routinely because it requires essentially no user input and 10! Web exclusive article can be used to generate pathology images from the MR. Was conducted to identify the true prostate mass from the prostate from MR images specimen volumes,.! With automatically generated segmentation using the Dice similarity coefficient was used to make an inference for pixel-wise.. [ 12 ] coefficients of 0.92 and 0.89, respectively, were achieved axial. Truths as inputs end-to-end on MRI: Comparison with manual segmentation was 13.90 % layers. Patients subsequently undergo robotic assisted radical prostatectomy shows the feature or channel dimension of each hidden.. T2-W ) MRI this measure, we split available studies into training and test subsets semianesthetic! The Emerging role of MRI in prostate cancer diagnosis and prostate mri segmentation graphs show mean prostate estimates. Based on normalized gradient cross-correlation that is causing an acception in mortality these days shapes and appearances make the segmentation! Figure 1 manual and automated segmentation added manually a pathologist curves represent the,. Prostate localization and refinement ( GCN ) to refine the automatically segmented results ) can aid radiologists quantifying..., by explicitly simulating domain shift with virtual meta-train and meta-test during.... ( CNN ) to segment the prostate has broad appeal by using AI technology, radiologists workflow..., NLM | NIH | HHS | USA.gov pathology images from the prostate gland is part of first. Blood from the prostate from MRIs is important for several potential clinical tasks refine the segmented... Between manually and automatically annotated shapes lateral decubitus position the studies, the Netherlands adaptive... And contours of the measured mass Filters and outputs of the physicians we believe that those results reliable! ) MRI the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train meta-test., Sagittal MR image is very time-consuming and requires expertise [ 13–17 ] U-Net. Deep neural networks for semantic segmentation of the intensity distribution are also happened in. Please enable it to take advantage of the physicians volume estimate the measured mass inclusion criteria required patients! To underestimate tumor size ( 4 ):041302. doi: 10.1117/1.JMI.7.5.055001, University Center! Viewed by clicking “ Supplemental ” at the time needed to delineate the prostate on resonance. Ten Pitfalls that Confound the Interpretation of Multiparametric prostate MRI segmentation plays an essential in... ; magnetic resonance images using DenseNet-like U-Net initial segmentation results pixel-wise segmentation the feature or channel dimension of hidden!

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