The specimen mass was used as ground truth for our data, as reported previously . 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. Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends. 2021 Jan;216(1):111-116. doi: 10.2214/AJR.19.22168. Prostate segmentation in MR images . We believe that this effect is small and the value of the endorectal coil is that it provides images with superior resolution for delineating the prostate boundaries, which is critical for accurate volume determinations. 6B —Bar graphs show mean prostate volume estimates and mean true prostate volume. In Section 2, the proposed prostate segmentation scheme for 3D MR images is explained in detail, including the basic concepts of region-based active contour model, the parametric deformable ellipsoid model, the proposed segmentation algorithm with shape information, and the automated shape penalty weight selection method. Prostate is outlined in green. S3 —Screen shot from video of fully automated segmentation tool (Medical Image Processing, Analysis and Visualization [MIPAV], Center for Information Technology, National Institutes of Health) in sample case. 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. (a) Filters of the first hidden layer (. 2020 Jun 3;20(11):3183. doi: 10.3390/s20113183. A, 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. A–I, Linear regression plots show prostate volume (PV) estimates derived from manual and automated segmentations and true prostate volumes extracted from prostatectomy specimens. 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). A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. Get the latest public health information from CDC: https://www.coronavirus.gov, Get the latest research information from NIH: https://www.nih.gov/coronavirus, Find NCBI SARS-CoV-2 literature, sequence, and clinical content: https://www.ncbi.nlm.nih.gov/sars-cov-2/. Our study has several limitations. In this scheme, each of three segmentations are spatially combined together into a single probability map, where each of the segmentations has a vote of one third. 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. 2A —Prostate segmentation visualization modes. Prostate Volumes Derived From MRI and Volume-Adjusted Serum Prostate-Specific Antigen: Correlation With Gleason Score of Prostate Cancer, MR imaging of the prostate gland: normal anatomy, Review. 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. This concept is illustrated in Figure 5, where the portions of the prostate outside the dashed lines in Figure 5C are not considered when computing the partial Dice similarity coefficient. Recently, Bulman et al. The 3D mold is used to generate pathology images from the prostate biopsy. The root mean squared error for automatic segmentation was 13.10%. Automating this process could result in highly accurate yet timely prostate volume determinations that could be incorporated routinely into clinical interpretations. 1 —Automated prostate segmentation consists of two steps: localization and refinement. Jia et al. The learned CNN model can be used to make an inference for pixel-wise segmentation. Anecdotally, this automated segmentation system performs well on images obtained without an endorectal coil. 8). 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. Fig. The data supplement accompanying this web exclusive article can be viewed by clicking “Supplemental” at the top of the article. The qualitative results of the proposed method. Prostate volume determinations based on the ellipsoid formula are often inaccurate because the shape of the prostate varies dramatically . Thus, this method avoids subjective differences among different viewers and yields a highly reproducible result. However, the effectiveness of these methods is often limited by inadequate semantic discrimination and spatial context modeling. Previous studies have reported various segmentation methods of prostate MR images. Keywords: fully automated segmentation, MRI, prostate, segmentation, volume. 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 absolute error (Δm = me – m) and relative error (δm = Δm / m) were calculated for each of the studies. Prostate MRI volume segmentation is a challenging task due to the wide range of appearance, also different scanning approaches. The imaging measurements generally rely on triplanar linear measurements to calculate prostate volume using the following formula for the volume of an ellipsoid: The DRE is often inaccurate because it is based on a subjective estimate of the examiner. Estimation of Radiation Exposure for Brain Perfusion CT: Standard Protocol Compared With Deviations in Protocol, Original Research. Therefore, many research efforts have been conducted to improve | Box 85500, 3508 GA Utrecht, The Netherlands. 2020 May 11;12(5):1204. doi: 10.3390/cancers12051204. Manual delineation of prostate in MR image is very time-consuming and depends on the subjective experience of the physicians. | The inclusion criteria required that patients subsequently undergo robotic assisted radical prostatectomy. Keywords: Although the Dice similarity coefficient is a popular measure of segmentation accuracy, its major drawback is that manually drawn contours are inaccurate in the surface regions tangent to the image viewing plane—for example, the base and apex of the prostate on axial images. Several Sectionations Of The Prostate In Mri Truthfulnesss Essay. If 3D volumetric representations of manually and automatically found shapes are denoted as Vx and Vy, respectively, the Dice similarity coefficient (DSC) can be computed as follows: The value of the Dice similarity coefficient can vary between 0.0 (no overlap between the shapes) and 1.0 (perfect overlap); larger values correspond to better spatial agreement between the manually and automatically annotated shapes. 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. However, Fortschr Röntgenstr 2020; DOI: 10.1055/a-1290-8070. A, Axial MR image with manually drawn contour showing prostate (green). Prostate cancer (PCa) is the most common cancer in men and is accountable for the second most cancer-related deaths [ 1 ]. 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. 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. Segmentation is useful for various tasks, e.g. 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. Would you like email updates of new search results? The 3D mold is fabricated via 3D printing. Volumetric Prostate Segmentation from MRI using FCNN. B, Two-dimensional cross sections were overlaid on original MR image slice to produce this image. OBJECTIVE. 2018 Nov;45(11):5129-5137. doi: 10.1002/mp.13221. 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. 2020 Sep;7(5):055001. doi: 10.1117/1.JMI.7.5.055001. Filters and outputs of the first hidden layer of the PSNet. 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). In this measure, we exclude the portions in the automated segmentation that do not have corresponding manual contours. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI. Fig. The prostate cancer is sole the distemper that is causing an acception in mortality these days. The volume of the segmented prostate is automatically provided. March 2019; DOI: 10.13140/RG.2.2.12635.18721 The objective of our study was to compare calculated prostate volumes derived from tridimensional MR measurements (ellipsoid formula), manual segmentation, and a fully automated segmentation system as validated by actual prostatectomy specimens. 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. HHS Lee DK, Sung DJ, Kim CS, Heo Y, Lee JY, Park BJ, Kim MJ. Deep learning has been identified as a potential new technology for the delivery of precision radiotherapy in prostate cancer, where accurate prostate segmentation helps … Bardis MD, Houshyar R, Chang PD, Ushinsky A, Glavis-Bloom J, Chahine C, Bui TL, Rupasinghe M, Filippi CG, Chow DS. Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. Search for more papers by this author. It employs the technique that combines image processing and computer aided design to construct a high resolution 3D prostate surface … Regions of interest were drawn on each slice of each plane using software (Medical Image Processing, Analysis and Visualization [MIPAV], Center for Information Technology, National Institutes of Health). The partial mean Dice similarity coefficient (which excludes slices that were not annotated) for triplane automated segmentations ranged between 0.90 and 0.92, whereas the full mean Dice similarity coefficient (which includes all slices) ranged between 0.83 and 0.89, with 0.89 representing the axial full mean Dice similarity coefficient. All analyses were conducted using statistics software (SAS version 6.0.1, SAS Institute). 5B —Partial Dice similarity coefficient. Electronic mail: email@example.com. We propose a cascade method for prostate segmentation. 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 . Epub 2017 Feb 24. Prostate volume estimates obtained with a fully automated 3D segmentation tool based on normalized gradient fields cross-correlation and graph-search refinement can yield highly accurate prostate volume estimates in a clinically relevant time of 10 seconds. Thus, MR images can also be used to effectively estimate the prostate mass. Summary of the use case. For modeling we used linear regression in the following form: where α is a multiplicative component that roughly represents prostate tissue density and β is an additive component to account for variation in measured mass due to the seminal vesicles and the excess of the extracted tissue. 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. As benign prostatic hyperplasia develops, the prostate evolves from a cone-shaped organ to a more spheric organ that often includes an eccentrically enlarged median lobe that is not accounted for by the ellipsoid formula. De-identified patient number, series instance UID of ultrasound, and series instance UID of MR images associated with the biopsy core. In another experiment we estimated how accurately we can predict true prostate mass from the prostate volume obtained using MR images. In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. A, Three-dimensional surface rendering shows prostate (green). 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. National Center for Biotechnology Information, Unable to load your collection due to an error, Unable to load your delegates due to an error. However, those approaches mainly paid attention to features and contexts within each si… to accurately localize prostate boundaries for radiotherapy or to … Location and contours of biopsy targets can be added manually. It is also likely, however, that the ex vivo specimen is somewhat smaller because of the loss of blood from the gland. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. J Med Imaging (Bellingham). Segmentation performed by those with less experience in prostate MRI appear to underestimate tumor size. Fig. Automatic segmentation can help to reduce the cost. Output of (a) the fourth layer and (b) the fifth layer of the PSNet. 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.). 6A —Bar graphs show mean prostate volume estimates and mean true prostate volume. 2020 May 1;20(1):33. doi: 10.1186/s40644-020-00311-4. 7). Measurements based on the DRE are subjective and difficult to reproduce. Cancer Imaging. Schelb P, Tavakoli AA, Tubtawee T et al. Segmentation of the prostate from MRIs is important for several potential clinical tasks. A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. 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. Prostate volume of the patient at the time of biopsy, as measured via MRI prostate segmentation. Then this probability map is thresholded at 0.5, denoted henceforth as TPM 0.5 as shown in Figure 4. In particular, detailed MR images allow to evaluate the prostate and determine the presence of diseases. 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 … Copyright © 2013-2020, American Roentgen Ray Society, ARRS, All Rights Reserved. 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. Fig. B, Sketch shows probability map thresholded at level of 0.5. 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. Prostate segmentation of MR volumes is a very important task for treatment planning and image-guided brachytherapy and radiotherapy. A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. We applied HED segmentation to orthogonal prostate images, and generated a high-resolution 3D prostate surface from the low-resolution MR images. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. 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. Martin et al. Epub 2020 Oct 14. AI-assisted MRI segmentation Deep learning boost for prostate cancer workflow Prostate cancer radiotherapy treatments guided by MRI are increasingly being explored to help improve patient outcomes and reduce toxicities after treatment. See this image and copyright information in PMC. 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. The segmentation algorithm consists of two sequential steps: prostate localization and prostate contour refinement as shown in Figure 1. Deformation and variations of the intensity distribution are also happened. Fu Y, Mazur TR, Wu X, Liu S, Chang X, Lu Y, Li HH, Kim H, Roach MC, Henke L, Yang D. Med Phys. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically.  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. For this study, only triplane T2-weighted TSE MR images were used for volume determinations. Fig. The Dice similarity coefficient was used to quantify spatial agreement between manual segmentation and automated segmentation. The relative error in the prostate mass estimate is the upper bound of the error in physical volume estimate because the density variation does not have an impact on the volume. An additional issue is that the manual segmentations were performed by a single experienced operator. Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. To fairly compare an automatic segmentation with a set of manually drawn contours, we introduce the concept of a partial Dice similarity coefficient. Segmentation is also a necessary step in fusing MRI to other imaging studies such as ultrasound (for biopsy or therapy), PET (for diagnosis), or future MRI studies (for longitudinal studies). The model consists of two stage. 2017 Oct;4(4):041302. doi: 10.1117/1.JMI.4.4.041302. Purpose. A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. 2020 Jun;214(6):1229-1238. doi: 10.2214/AJR.19.22254. 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.
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