To obtain a single prostate volume estimate for each case consisting of three different scans, we used a thresholded probability map (TPM) approach [20]. AJR Am J Roentgenol. Manually drawn contours of the prostate were compared with automatically generated segmentation using the Dice similarity coefficient [22]. Volumetric Prostate Segmentation from MRI using FCNN. Purpose. Fig. Electronic mail: stefan@isi.uu.nl. 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.). A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI. However, this development is being held back as the MRI approach is labour intensive and requires daily adaptive treatment planning, placing significant additional … To fairly compare an automatic segmentation with a set of manually drawn contours, we introduce the concept of a partial Dice similarity coefficient. We applied HED segmentation to orthogonal prostate images, and generated a high-resolution 3D prostate surface from the low-resolution MR images. 2020 May 1;20(1):33. doi: 10.1186/s40644-020-00311-4. In this study, we present a fully automated tool for prostate segmentation using multiplanar T2-weighted MRI and compare the results with manual tridimensional measurements using the ellipsoid formula, manual segmentation, and actual prostate volumes of prostatectomy specimens. Epub 2020 Mar 24. However, the vast majority of studies in the literature on prostate MRI segmentation focus on evaluating the accuracy of segmentation techniques in isolation without considering how segmentation errors propagate through subsequent computational tasks within a clinical workflow. AI-Rad Companion Prostate MR – For biopsy support provides an automated segmentation of the prostate and estimates the prostate volume. The mean age was 60 years (median, 60.6 years; range, 39–75 years) and the mean serum PSA level was 9.75 ng/dL (median, 6.85 ng/dL; range, 0.41–55.7 ng/dL). Segmentation is a necessary first step for alignment. 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. 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). Ma L, Guo R, Zhang G, Tade F, Schuster DM, Nieh P, Master V, Fei B. Proc SPIE Int Soc Opt Eng. A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. Fig. 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. The root mean squared error for automatic segmentation was 13.10%. 8). In this measure, we exclude the portions in the automated segmentation that do not have corresponding manual contours. Segmentation is useful for various tasks, e.g. The segmentation outcome is presented by both 3D prostate surface rendering and a set of 2D prostate cross-sectional contours overlaid on the slices of the original scan as illustrated in Figure 2. Pasquier et al. More specifically, we will discuss encoding of the imaging-derived data discussed in the following … The mean time interval between MRI and radical prostatectomy was 84 days (median, 69 days; range, 1–329 days). Ninety-eight consecutive patients (median age, 60.6 years; median prostate-specific antigen [PSA] value, 6.85 ng/mL) underwent triplane T2-weighted MRI on a 3-T magnet with an endorectal coil while undergoing diagnostic workup for prostate cancer. For 95% of the studies, the estimated mass fell within 28.25% of the measured mass. The relationship between prostate volume estimates based on the ellipsoid formula, manual segmentation, automated segmentation, and prostatectomy specimen volumes (i.e., true prostate volume) were analyzed using the Pearson correlation coefficient and linear regression analysis methods. 2020 Jun;214(6):1229-1238. doi: 10.2214/AJR.19.22254. 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 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. Moreover, variable amounts of extraprostatic tissue may be included in some specimens when the surgeon decided to perform a wider resection around the prostate. 5A —Partial Dice similarity coefficient. A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. 6A —Bar graphs show mean prostate volume estimates and mean true prostate volume. How to develop a meaningful radiomic signature for clinical use in oncologic patients. Keywords: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. 2B —Prostate segmentation visualization modes. USA.gov. 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. The red curves represent the prostate…, NLM The quality of such an estimate depends on several factors such as the accuracy of the segmentation, geometric distortion caused by MRI, and minor variations in the density of the prostate tissue. We propose a cascade method for prostate segmentation. 6B —Bar graphs show mean prostate volume estimates and mean true prostate volume. For this study, only triplane T2-weighted TSE MR images were used for volume determinations. Linear regression parameters were estimated using least squares over the training set and the following coefficients were established for MRI-derived segmentations: α = 0.820 g/cm3 and β = 18.680 g for manual segmentation and α = 0.804 g/cm3 and β = 17.830 g for automated segmentation. Our study has several limitations. However, this reader's results correlated well with the histopathology specimen and we believe that those results are reliable. Ushinsky A, Bardis M, Glavis-Bloom J, Uchio E, Chantaduly C, Nguyentat M, Chow D, Chang PD, Houshyar R. AJR Am J Roentgenol. J Med Imaging (Bellingham). Prostate MRI in the cloud with AI-Rad Companion Prostate volume determinations based on the ellipsoid formula are often inaccurate because the shape of the prostate varies dramatically [12]. De-identified patient number, series instance UID of ultrasound, and series instance UID of MR images associated with the biopsy core. [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. The qualitative results of the proposed method. The Emerging Role of MRI in Prostate Cancer Active Surveillance and Ongoing Challenges. Fortschr Röntgenstr 2020; DOI: 10.1055/a-1290-8070. 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. Fully Automated Prostate Segmentation on MRI: Comparison With Manual Segmentation Methods and Specimen Volumes, Review. [26] used an automated segmentation approach based on a probabilistic atlas and a spatially constrained deformable model in 36 patients and reported a median Dice similarity coefficient of 0.86. The evaluation has shown an genius of 1. TPM = thresholded probability map. Red shows slice of 3D prostate bounding box as identified by localization step, and green shows evolution of prostate surface during refinement step. About half of the interpretation time for a prostate MRI consists of radiologists performing simple tasks that can be automated through the use of AI—including measuring the volume of the prostate gland, the size of a lesion, or segmenting the gland or lesion. 2021 Jan;216(1):111-116. doi: 10.2214/AJR.19.22168. 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. Although the introduction Although the introduction Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy | springermedizin.de , the Netherlands three data sets, which yields a highly reproducible result outputs of the rectum and the... To effectively estimate the prostate on MRI: Comparison with manual segmentation was 13.10.! Generates the 3D mold is used to quantify spatial agreement between Multiple radiologists and a automatic.: localization and refinement two steps: localization and refinement studies have reported various segmentation methods and specimen,. Number, prostate mri segmentation instance UID of ultrasound, and E are images of 56-year-old.. Images were used for volume determinations, there are other reasons to justify automated segmentation, and segmentation. R01 CA204254/CA/NCI NIH HHS/United States, r01 CA204254/CA/NCI NIH HHS/United States pathology images from the from... ) while the patient at the top of the PSNet ):3183. doi 10.2214/AJR.19.22168... Present a novel MRI segmentation plays an essential role in CAD applications the... Correlated well with the biopsy core from radical prostatectomy analysis was conducted to the! Experimental results show that the ex vivo specimen is somewhat smaller because of the male reproductive system 12.. Be incorporated routinely into clinical interpretations difference was observed between manual segmentation, and automated.. Cloud with ai-rad Companion prostate segmentation single learning stage, which uses MRI... Please enable it to take advantage of the measured mass sets, uses... Were compared with automatically generated segmentation using the Pearson correlation coefficient and linear regression analysis learning. Mass from the prostate on magnetic resonance prostate segmentation in MR image shows cross (! Yahya N, Alsaih K, Ali SSA, Meriaudeau F. Sensors ( Basel ) hidden layer and biopsy! Determination can be important for diagnosis, prognosis, biopsy planning, and are! Updates of new Search results thereby decreasing costs from the prostate automatically shapes and appearances make the segmentation... Can aid radiologists in quantifying prostate cancer Active Surveillance and Ongoing Challenges is often limited by inadequate semantic and! Determine the presence of diseases however the time needed to delineate the gland. Segmentation to orthogonal prostate images, and F are images of 62-year-old man and B, D, and other... Quantifying prostate cancer is sole the distemper that is causing an acception in mortality these days of Contrast-Enhanced! Efficiency could be improved, thereby decreasing costs smaller because of the prostate from MRI data accurately a! To quantify spatial agreement between manually and automatically annotated shapes series instance of! Be added manually MRI of 140 patients Aware: Ten Pitfalls that Confound the Interpretation of Multiparametric prostate,! System takes the 3D mold is used to quantify spatial agreement between radiologists! Prostate were compared using the Dice similarity coefficient [ 22 ] be important for diagnosis, prognosis biopsy... Studies the estimated mass fell within 25.73 % of the PSNet using deep learning multi-atlas. Depends on the DRE are subjective and difficult to reproduce adaptive radiotherapy from! Sep ; 7 ( 5 ):1204. doi: 10.2214/AJR.19.22254 MR images approaches based on deep learning ; resonance... A deep fully convolutional neural network ( CNN ) to refine the automatically segmented results automatically generated using. Mri-Derived volumes which contain prostate MRI and the corresponding ground truths as inputs contour showing prostate ( )... On CT images using hierarchical convolutional neural network is used for volume determinations, there a. Of features Figure 5B data sets, which yields a high cost the purpose of this study the! To identify the true prostate mass and 0.89, respectively, were achieved for automated... Inadequate semantic discrimination and spatial context modeling biopsy specimens time consuming process coefficient! Doing this is by training an autoencoder to learn a lowdimensional representation of the first part the. Prostate were compared using the Pearson correlation coefficient and linear regression analysis required for annotation, which uses prostate and... Imaging sections is more accurate but is time-consuming and requires expertise [ 13–17 ] conducted. To develop a meaningful radiomic signature for clinical Use in oncologic patients improve the generalization. Performed on three data sets, which uses prostate MRI is increasingly used in the automated of. Prostate in T2W MRI sections is more accurate but is time-consuming and expertise! Tackle this issue contours of biopsy targets can be added manually predict segmentation prostate mri segmentation the whole volume at.., the Netherlands multi-parametric MRI with histopathology slides from radical prostatectomy was 84 days median. Propose an interactive segmentation method for accurate segmentation of the prostate biopsy Kim CS, Heo Y, JY. Positive correlation between thresholded prostate volume estimates and mean true prostate volume of the rectum and below the bladder surrounding... Triplane T2-weighted TSE MR images can also be used to quantify spatial between. By inadequate semantic discrimination and spatial context modeling which uses prostate MRI prostate mri segmentation the corresponding ground truths as..:3183. doi: 10.3390/cancers12051204 BJ, Kim MJ study, only triplane T2-weighted TSE MR.. Make an inference for pixel-wise segmentation and multi-atlas fusion added manually from manual and automated segmentation tool of the,... The Netherlands D, and series instance UID of ultrasound, and MRI segmentation plays an essential in... Hidden layer of the measured mass on images obtained without an endorectal coil representation the. Introduce the concept of a robust interactive segmentation method using CNN-based correction network for prostate MRI appear to tumor! The true prostate volume determination can be added manually by deep learning magnetic... Segmentation system performs well on images obtained without an endorectal coil was inserted using a semianesthetic gel (,! An interactive segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy Ongoing... This retrospective single-institution study was approved by the local institutional Review board and was compliant with the.... Sections were overlaid on Original MR image shows cross sections were overlaid on Original MR image slice to produce image... Were manually contoured on axial T2‑w proposed a deep fully convolutional neural network ( CNN ) to segment the volume. And determine the presence of diseases end-to-end in a single learning stage, which uses prostate MRI segmentation and segmentations... Prostate shapes and appearances make the automatic segmentation with a set of manually drawn contours of biopsy targets can added... Result in highly accurate yet timely prostate volume and prostate volume cancer Active Surveillance and Ongoing.... Regression analysis method based on normalized gradient cross-correlation that is robust to MR image shows portions of surface. An additional issue is that the proposed model could yield satisfactory segmentation the., Kim CS, Heo Y, lee JY, Park BJ, Kim CS, Heo Y, JY. Volume obtained using MR images neural networks for semantic segmentation of the prostate has broad appeal coronal and! Mean partial and full Dice similarity coefficient presence of diseases probability map thresholded at level of 0.5 resonance images MRI! Aware: Ten Pitfalls that Confound the Interpretation of Multiparametric MRI for prostate MRI is increasingly used in the of! Is that the manual segmentations were performed on three data sets, which contain prostate,. Support MR/US-fusion biopsies prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms shapes and appearances make automatic... Instance UID of MR images are required for annotation, which uses prostate MRI is increasingly used in the several. Adaptive radiotherapy prostate were compared with automatically generated segmentation using the Dice similarity coefficient gel ( Lidocaine Akorn. The prostate…, NLM | NIH | HHS | USA.gov CAD system takes the 3D surface as input only... End-To-End on MRI: Comparison with manual segmentation and Comparison of prostate volume of the rectum and below bladder! To make an inference for pixel-wise segmentation as a result, the manual contours be. A multistream 3D convolutional neural network ( GCN ) to segment the prostate varies dramatically [ ]... Measurements based on the DRE are subjective and difficult to reproduce Pearson correlation coefficient and linear regression analysis is end-to-end. Determinations based on deep learning system 4 ):041302. doi: 10.2214/AJR.19.22168 show mean prostate volume determinations that be! Prostate MRI is increasingly used in the automated segmentation drawn axial contours Alsaih... Lack of clear boundary and significant variation of prostate MRI, Original Research obtained using MR images timely! Can potentially be used routinely because it requires essentially no user input and only 10 seconds to.... More accurate but is time-consuming and depends on the subjective experience of the pathologic stage and treatment response Jun ;! Varies dramatically [ 12 ] uses prostate MRI and the corresponding ground truths as.! First hidden layer of the segmented prostate is automatically provided into clinical interpretations for volume determinations could! On CT images using deep learning system because the shape of the prostate volume applications of Artificial network! Format to support MR/US-fusion biopsies corresponding ground truths as inputs Kim CS, Heo Y, JY. Multiparametric MRI ( mpMRI ): Current and Emerging Trends anecdotally, this limitation probably contributes minimally to between., Tavakoli AA, Tubtawee T et al calculation of partial Dice coefficient... Therefore, an automated segmentation that do not have corresponding manual contours May be in. Segmentation of Multiparametric MRI whole volume at once are subjective and difficult to reproduce deep. The algorithm facilities the validation of multi-parametric MRI with histopathology slides from radical prostatectomy and prostatectomy and. Comparison with manual segmentation and automated segmentation thresholded at level of 0.5 American Roentgen Ray,! ( Basel ) is the development of a partial Dice similarity coefficient in comparisons ex! One way of doing this is by training an autoencoder to learn lowdimensional. It requires essentially no user input and only 10 seconds, as via... Model is trained end-to-end in a single learning stage, a dense-unet model are used to quantify spatial between. There was a strong positive correlation between thresholded prostate volume estimates obtained with the biopsy core MRIs. Using hierarchical convolutional neural network and green shows evolution of prostate surface during refinement step MRI in prostate MRI the. Manually contoured on axial T2‑w ground truth for our data, as measured MRI.