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. Moreover, there was a strong positive correlation between thresholded prostate volume estimates derived from manual and automated segmentations (Table 3). 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). We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. 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]. Solidworks CAD system takes the 3D surface as input and generates the 3D prostate mold as shown in Figure 5. The number shows the feature or channel dimension of each hidden layer. Green shows outline of prostate. The endorectal coil was inserted using a semianesthetic gel (Lidocaine, Akorn) while the patient was in the left lateral decubitus position. Fig. Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System. Radiologist, Be Aware: Ten Pitfalls That Confound the Interpretation of Multiparametric Prostate MRI, Original Research. Fig.  |  This retrospective single-institution study was approved by the local institutional review board and was compliant with the HIPAA. The average total segmentation time for each patient is approximately 10 seconds, as shown in Figure S3. Recently, deep learning methods have been proposed to tackle this issue. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. The qualitative results of the proposed method. More specifically, we will discuss encoding of the imaging-derived data discussed in the following … Recently, Bulman et al. The current methods of estimating prostate volume include physician estimation based on a digital rectal examination (DRE) and imaging measurements based on ultrasound, CT, or MRI. The red curves represent the prostate…, NLM The protocol included triplanar T2-weighted turbo spin-echo (TSE) MRI, diffusion-weighted MRI, 3D MR spectroscopy, axial unenhanced T1-weighted MRI, and axial 3D fast-field echo dynamic contrast-enhanced MRI sequences. Pasquier et al. 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. Segmentation is useful for various tasks, e.g. Organ image segmentation can be improved by implementing prior knowledge about the anatomy. 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. The absolute error (Δm = me – m) and relative error (δm = Δm / m) were calculated for each of the studies. 7). Fig. The segmentation algorithm consists of two sequential steps: prostate localization and prostate contour refinement as shown in Figure 1. The 3D mold is fabricated via 3D printing. This tool will assist in developing a broad range of applications including routine prostate volume estimations, image registration, biopsy guidance, and decision support systems. 2020 Jun 3;20(11):3183. doi: 10.3390/s20113183. The World heartiness Organization (WHO) statistics declares that this cancer ends on the promote continual tooth amongst humanity and on the fourth attribute amongst twain genders. Author to whom correspondence should be addressed. Fig. radiotherapy target definition. Purpose. B, Sketch shows probability map thresholded at level of 0.5. Prostate is outlined in green. However, those approaches mainly paid attention to features and contexts within each si… A separate analysis was conducted to identify the true prostate volume and MRI volume estimation errors (Appendix 1). Fortschr Röntgenstr 2020; DOI: 10.1055/a-1290-8070. 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. Thus, this method avoids subjective differences among different viewers and yields a highly reproducible result. 2020 Jun;214(6):1229-1238. doi: 10.2214/AJR.19.22254. 5A —Partial Dice similarity coefficient. The Dice similarity coefficient was used to quantify spatial agreement between manual segmentation and automated segmentation. 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. 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 Epub 2017 Feb 24. In this use case we will summarize the approach to encode segmentations of various structures and measurements derived using those segmentations from multi-parametric Magnetic Resonance Imaging (MRI) of the prostate. A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. AJR Am J Roentgenol. Location and contours of biopsy targets can be added manually. Ushinsky A, Bardis M, Glavis-Bloom J, Uchio E, Chantaduly C, Nguyentat M, Chow D, Chang PD, Houshyar R. AJR Am J Roentgenol. It is also likely, however, that the ex vivo specimen is somewhat smaller because of the loss of blood from the gland. Segmentation is a necessary first step for alignment. Society of Photo-Optical Instrumentation Engineers. 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. Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. Our study has several limitations. 2017 Oct;4(4):041302. doi: 10.1117/1.JMI.4.4.041302. The learned CNN model can be used to make an inference for pixel-wise segmentation. A, C, and E are images of 62-year-old man and B, D, and F are images of 56-year-old man. 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). How to develop a meaningful radiomic signature for clinical use in oncologic patients. Klein et al. B, Sagittal MR image shows cross sections (green lines) of multiple manually drawn axial contours. To obtain a single prostate volume estimate for each case consisting of three different scans, we used a thresholded probability map (TPM) approach [20]. Cheng R, Roth HR, Lay N, Lu L, Turkbey B, Gandler W, McCreedy ES, Pohida T, Pinto PA, Choyke P, McAuliffe MJ, Summers RM. Schelb P, Tavakoli AA, Tubtawee T et al. 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. 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. C, Sagittal MR image shows portions of prostate removed (dashed lines) for calculation of partial Dice similarity coefficient. 2021 Jan;216(1):111-116. doi: 10.2214/AJR.19.22168. 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. The purpose of this study is the development of a robust interactive segmentation method for accurate segmentation of the prostate from MR images. [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. Methods. 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. 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