Four-dimensional (4D) radiotherapy is the explicit inclusion of the temporal changes in anatomy during the imaging, planning, and delivery of radiotherapy. One key component of 4D radiotherapy planning is the ability to automatically ("auto") create contours on all of the respiratory phase computed tomography (CT) datasets comprising a 4D CT scan, based on contours manually drawn on one CT image set from one phase. A tool that can be used to automatically propagate manually drawn contours to CT scans of other respiratory phases is deformable image registration. The purpose of the current study was to geometrically quantify the difference between automatically generated contours with manually drawn contours. Four-DCT data sets of 13 patients consisting of ten three-dimensional CT image sets acquired at different respiratory phases were used for this study. Tumor and normal tissue structures [gross tumor volume (GTV), esophagus, right lung, left lung, heart and cord] were manually drawn on each respiratory phase of each patient. Large deformable diffeomorphic image registration was performed to map each CT set from the peak-inhale respiration phase to the CT image sets corresponding with subsequent respiration phases. The calculated displacement vector fields were used to deform contours automatically drawn on the inhale phase to the other respiratory phase CT image sets. The code was interfaced to a treatment planning system to view the resulting images and to obtain the volumetric, displacement, and surface congruence information; 692 automatically generated structures were compared with 692 manually drawn structures. The auto- and manual methods showed similar trends, with a smaller difference observed between the GTVs than other structures. The auto-contoured structures agree with the manually drawn structures, especially in the case of the GTV, to within published interobserver variations. For the GTV, fractional volumes agree to within 0.2+/-0.1, center of mass displacements agree to within 0.5+/-1.5 mm, and agreement of surface congruence is 0.0+/-1.1 mm. The surface congruence between automatic and manual contours for the GTV, heart, left lung, right lung and esophagus was less than 5 mm in 99%, 94%, 94%, 91% and 89%, respectively. Careful assessment of the performance of automatic algorithms is needed in the presence of 4D CT artifacts.
The segmentation of the hippocampus in Magnetic Resonance Imaging (MRI) has been an important procedure to diagnose and monitor several clinical situations. The precise delineation of the borders of this brain structure makes it possible to obtain a measure of the volume and estimate its shape, which can be used to diagnose some diseases, such as Alzheimer's disease, schizophrenia and epilepsy. As the manual segmentation procedure in three-dimensional images is highly time consuming and the reproducibility is low, automated methods introduce substantial gains. On the other hand, the implementation of those methods is a challenge because of the low contrast of this structure in relation to the neighboring areas of the brain. Within this context, this research presents a review of the evolution of automatized methods for the segmentation of the hippocampus in MRI. Many proposed methods for segmentation of the hippocampus have been published in leading journals in the medical image processing area. This paper describes these methods presenting the techniques used and quantitatively comparing the methods based on Dice Similarity Coefficient. Finally, we present an evaluation of those methods considering the degree of user intervention, computational cost, segmentation accuracy and feasibility of application in a clinical routine.
Purpose: A necessary tool to facilitate automated four‐dimensional and adaptive radiotherapy planning is deformable image registration (DIR). The purpose of the current study was to quantify the accuracy of a DIR algorithm by comparing automatically transferred and manually segmented structures on 4DCT images. Method and Materials: 780 structures were manually segmented on thirteen patient 4DCT image sets each consisting of 10 respiratory phases. A large deformable diffeomorphic DIR algorithm, integrated with a commercial treatment planning system, was used to map each CT set from the inspiration respiratory phase CT image set respiratory phase images. The calculated displacement vector fields were used to deform and transfer structures defined on the inspiration CT to the other respiratory phase CT image sets. The manually and automatically segmented structures were compared using volumetric, displacement, and surface congruence metrics. Results: Deformation with respiration was observed for the lung tumor and normal tissues. This deformation was verified by examining the mapping of high contrast objects, such as the lungs and cord, between image sets. The auto‐ and manual methods showed similar trends, with a smaller difference observed between the GTVs than other structures. The auto‐contoured structures were more consistent both in terms of centroid displacement and volume as a function of respiratory phase than manual contours. 1.6% of the time, deficiencies of manual contouring has been detected using auto contouring. Image artifacts play a crucial role in auto contouring. Conclusion: An automated system is established to auto‐contour structures starting from one 4DCT image phase to other 4DCT image phases. The auto‐contoured structures generally agree with the manually drawn structures. However the auto‐contoured structures are more consistent in trajectory and volume, and also highlighted some large errors in the manually drawn contours. Careful assessment is needed in the presence of 4DCT artifacts.
Purpose: Four‐dimensional (4D) radiotherapy is the explicit inclusion of the temporal changes in anatomy during the imaging, planning and delivery of radiotherapy. One key component of 4D radiotherapy planning, is the ability to auto contour the individual respiratory phase CT datasets (up to ten in total) comprising a 4D computed tomography (CT) scan. A tool that can be used to automatically generate such contours (based on contours manually drawn on a single CT phase) is deformable image registration. The purpose of the current study was to compare automatically generated contours with manually drawn contours. Method and Materials: Two out of ten patient 4D CT data sets have completed this study. The 4D CT scans consisted of a series of ten 3D CT image sets acquired at different respiratory phases. Large deformable diffeomorphic image registration was performed to map each CT set from the peak‐inhale respiration phase to the CT image sets corresponding with subsequent respiration phases. The calculated displacement vector fields were used to deform contours defined on the peak‐inhale CT automatically to the other respiratory phase CT image sets. Auto‐contouring was performed on each of the ten 3D image sets via automated scripts. Treatment planning system, Pinnacle version 7.7 was interfaced with automated scripts to view the resulting images and to obtain the volumetric and displacement information. Results: Deformation with respiration was observed for the lung tumor and normal tissues. This deformation was verified by examining the mapping of high contrast objects, such as the lungs and cord, between image sets. Conclusion: An automated system is established to auto ‐ contour the ROI's starting form the ROI's in the inhale phase to the other phases of the respiratory motion using Pinnacle Treatment planning system. Auto‐contoured organs and the GTV in the Thorax agree with the manually drawn ROI's.
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