AUTOMATIC ORGAN DELINEATION OF COMPUTED TOMOGRAPHY IMAGES FOR RADIOTHERAPY PLANNING IN PROSTATE CANCER - An Overview
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paper_title: Four‐dimensional cone‐beam computed tomography using an on‐board imager

paper_content:

On-board cone-beam computed tomography (CBCT) has recently become available to provide volumetric information of a patient in the treatment position, and holds promises for improved target localization and irradiation dose verification. The design of currently available on-board CBCT, however, is far from optimal. Its quality is adversely influenced by many factors, such as scatter, beam hardening, and intra-scanning organ motion. In this work we quantitatively study the influence of organ motion on CBCT imaging and investigate a strategy to acquire high quality phase-resolved [four-dimensional (4D)] CBCT images based on phase binning of the CBCT projection data. An efficient and robust method for binning CBCT data according to the patient's respiratory phase derived in the projection space was developed. The phase-binned projections were reconstructed using the conventional Feldkamp algorithm to yield 4D CBCT images. Both phantom and patient studies were carried out to validate the technique and to optimize the 4D CBCT data acquisition protocol. Several factors that are important to the clinical implementation of the technique, such as the image quality, scanning time, number of projections, and radiation dose, were analyzed for various scanning schemes. The general references drawn from this study are: (i) reliable phase binning of CBCT projectionsmore » is accomplishable with the aid of external or internal marker and simple analysis of its trace in the projection space, and (ii) artifact-free 4D CBCT images can be obtained without increasing the patient radiation dose as compared to the current 3D CBCT scan.« less
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paper_title: Model-based segmentation of medical imagery by matching distributions

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The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image. This allows for a fast, principled algorithm. We present promising results on difficult imagery for 3-D computed tomography images of the male pelvis for the purpose of image-guided radiotherapy of the prostate.
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paper_title: Image segmentation in treatment planning for prostate cancer using the region growing technique

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The purpose of this study was to evaluate the performance of a region growing technique for segmenting prostate, bladder and rectum in CT images of prostate cancer patients. Prostate, bladder and rectum were segmented in all CT images of 10 patients using the region growing technique and manual tracing. Volumes of the above organs computed with the region growing technique were compared with those from manually traced images on a slice-by-slice basis. Measurement reproducibility of both segmentation techniques was evaluated using the data obtained from four independent observers. The region growing technique was 1.5 times faster than manual tracing. There was no statistical difference between the slice volumes of prostate, bladder and rectum obtained by the two segmentation techniques (p>0.05, paired Student's t-test). Correlation between slice volumes of all organs of interest provided both by region growing and by manual tracing was very good (prostate r2=0.84; bladder r2=0.93; rectum r2=0.85). An overa...
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paper_title: Guidelines for primary radiotherapy of patients with prostate cancer.

paper_content:

Background and purposes: The, appropriate application of 3-D conformal radiotherapy, intensity modulated radiotherapy or image guided radiotherapy for patients undergoing radiotherapy for prostate cancer requires a standardisation of target delineation as well as clinical quality assurance procedures. Patients and methods: Pathological and imaging studies provide valuable information on tumour extension. In addition, clinical investigations on patient positioning and immobilisation as well as treatment verification data offer an abundance of information. Results: Target volume definitions for different risk groups of prostate cancer patients based on pathological and imaging studies are provided. Available imaging modalities, patient positioning and treatment preparation studies as well as verification procedures are collected from literature studies. These studies are summarised and recommendations are given where appropriate. Conclusions: On behalf of the European Organisation for Research and Treatment of Cancer (EORTC) Radiation Oncology Group this article presents a common set of recommendations for external beam radiotherapy of patients with prostate cancer. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
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paper_title: Automated model-based organ delineation for radiotherapy planning in prostatic region.

paper_content:

PURPOSE ::: Organ delineation is one of the most tedious and time-consuming parts of radiotherapy planning. It is usually performed by manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional (3D) data set used for planning. In this paper, a 3D model-based approach to automated organ delineation is introduced that allows for a significant reduction of the time required for contouring. ::: ::: ::: METHODS AND MATERIALS ::: The presented method is based on an adaptation of 3D deformable surface models to the boundaries of the anatomic structures of interest. The adaptation is based on a tradeoff between deformations of the model induced by its attraction to certain image features and the shape integrity of the model. To make the concept clinically feasible, interactive tools are introduced that allow quick correction in problematic areas in which the automated model adaptation may fail. A feasibility study with 40 clinical data sets was done for the male pelvic area, in which the risk organs (bladder, rectum, and femoral heads) were segmented by automatically adapting the corresponding organ models. ::: ::: ::: RESULTS ::: In several cases of the validation study, minor user interaction was required. Nevertheless, a statistically significant reduction in the time required compared with manual organ contouring was achieved. The results of the validation study showed that the presented model-based approach is accurate (1.0-1.7 mm mean error) for the tested anatomic structures. ::: ::: ::: CONCLUSION ::: A framework for organ delineation in radiotherapy planning is presented, including automated 3D model-based segmentation, as well as tools for interactive corrections. We demonstrated that the proposed approach is significantly more efficient than manual contouring in two-dimensional slices.
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Title: AUTOMATIC ORGAN DELINEATION OF COMPUTED TOMOGRAPHY IMAGES FOR RADIOTHERAPY PLANNING IN PROSTATE CANCER - An Overview

Section 1: INTRODUCTION
Description 1: Provide an introduction to prostate cancer, the importance of radiotherapy, and the challenge of organ delineation in CT images for RTP.

Section 2: METHODOLOGY
Description 2: Detail the comprehensive literature search, inclusion/exclusion criteria, and the query used for selecting relevant studies.

Section 3: FINDINGS
Description 3: Present the outcomes of the selected studies, including the techniques used, the comparison between automatic and manual segmentation, and the expertise of the participants.

Section 4: DISCUSSION
Description 4: Analyze and compare the results of different studies, highlight the efficiency and accuracy of automatic segmentation, and discuss the limitations and future directions for improving segmentation algorithms.