3D delineation of prostate, rectum and bladder on MR images.

In radiotherapy planning, target volumes and organs at risk delineation are a tedious and time-consuming task. In this paper we address a method to assist the radiologist in this task. We developed a 3D deformable model for prostate segmentation and used a seeded region growing algorithm for bladder and rectum delineation on MR images. Evaluation of the methods is made by comparison of the results to manual delineation in 24 patients. The following parameters were measured: volume ratio (V R) (automatic/manual), volume overlap (V O) (ratio of the volume of intersection to the volume of union, optimal value=1), and correctly delineated volume (V C) (percent ratio of the volume of intersection to the manual defined volume, optimal value=100). For prostate the V R, V O and V C were 1.13 (+/-0.1), 0.78 (+/-0.05) and 94.75 (+/-3.3), respectively. For rectum, the V R, V O and V C were 0.97 (+/-0.1), 0.78 (+/-0.06) and 86.52 (+/-5), respectively. V R, V O and V C were 0.95 (+/-0.03), 0.88 (+/-0.03) and 91.29 (+/-3.1) for bladder, respectively.

[1]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  F. H. Lee,et al.  Segmentation of nasopharyngeal carcinoma (NPC) lesions in MR images. , 2005, International journal of radiation oncology, biology, physics.

[3]  Vladimir Pekar,et al.  Automated model-based organ delineation for radiotherapy planning in prostatic region. , 2004, International journal of radiation oncology, biology, physics.

[4]  Andrew Thall,et al.  A method and software for segmentation of anatomic object ensembles by deformable m-reps. , 2005, Medical physics.

[5]  He Wang,et al.  Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes. , 2005, International journal of radiation oncology, biology, physics.

[6]  L. F. Cazzaniga,et al.  Interphysician variability in defining the planning target volume in the irradiation of prostate and seminal vesicles. , 1998, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[7]  Edward L. Chaney,et al.  Histogram Statistics of Local Model-Relative Image Regions , 2005, DSSCV.

[8]  C. Fiorino,et al.  Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. , 1998, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[9]  Tao Zhang,et al.  Model-based segmentation of medical imagery by matching distributions , 2005, IEEE Transactions on Medical Imaging.

[10]  D. Dearnaley,et al.  Target volume definition in conformal radiotherapy for prostate cancer: quality assurance in the MRC RT-01 trial. , 2000, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[11]  J. Damilakis,et al.  Image segmentation in treatment planning for prostate cancer using the region growing technique. , 2001, The British journal of radiology.

[12]  Radhe Mohan,et al.  Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy. , 2004, International journal of radiation oncology, biology, physics.

[13]  Maximilien Vermandel,et al.  Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[14]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[15]  N. Ayache,et al.  Atlas-based automatic segmentation of MR images: validation study on the brainstem in radiotherapy context. , 2005, International journal of radiation oncology, biology, physics.

[16]  Andrew Mehnert,et al.  An improved seeded region growing algorithm , 1997, Pattern Recognit. Lett..

[17]  Sarang Joshi,et al.  Large deformation three-dimensional image registration in image-guided radiation therapy , 2005, Physics in medicine and biology.

[18]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[19]  K. Ling,et al.  Prostate Boundary Detection From Ultrasonographic Images , 2003, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[20]  Yueh-Yun Chi,et al.  Comparison of human and automatic segmentations of kidneys from CT images. , 2005, International journal of radiation oncology, biology, physics.

[21]  T. Mackie,et al.  Fast free-form deformable registration via calculus of variations , 2004, Physics in medicine and biology.

[22]  Keith J. Burnham,et al.  Automatic segmentation of clinical structures for RTP: Evaluation of a morphological approach , 2001 .