Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images

A new method for volume segmentation of PET images, based on possibility theory

18F-fluorodeoxyglucose positron emission tomography (18FDG PET) has become an essential technique in oncology. Accurate segmentation and uptake quantification are crucial in order to enable objective follow-up, the optimization of radiotherapy planning, and therapeutic evaluation. We have designed and evaluated a new, nearly automatic and operator-independent segmentation approach. This incorporated possibility theory, in order to take into account the uncertainty and inaccuracy inherent in the image. The approach remained independent of PET facilities since it did not require any preliminary calibration. Good results were obtained from phantom images [percent error =18.38% (mean) ± 9.72% (standard deviation)]. Results on simulated and anatomopathological data sets were quantified using different similarity measures and showed the method was efficient (simulated images: Dice index =82.18% ± 13.53% for SUV =2.5 ). The approach could, therefore, be an efficient and robust tool for uptake volume segmentation, and lead to new indicators for measuring volume of interest activity.

Volume quantification by fuzzy logic modelling in freehand ultrasound imaging

INTRODUCTION

Many algorithms exist for 3D reconstruction of data from freehand 2D ultrasound slices. These methods are based on interpolation techniques to fill the voxels from the pixels. For quantification purposes, segmentation is involved to delineate the structure of interest. However, speckle and partial volume effect errors can affect quantification.

OBJECTIVE

This study aimed to assess the effect of the combination of a fuzzy model and 3D reconstruction algorithms of freehand ultrasound images on these errors.

METHODS

We introduced a fuzzification step to correct the initial segmentation, by weighting the pixels by a distribution function, taking into account the local gray levels, the orientation of the local gradient, and the local contrast-to-noise ratio. We then used two of the most wide-spread reconstruction algorithms (pixel nearest neighbour (PNN) and voxel nearest neighbour (VNN)) to interpolate and create the volume of the structure. Finally, defuzzification was used to estimate the optimal volume.

VALIDATION

B-scans were acquired using 5 MHz and 8 MHz ultrasound probes on ultrasound tissue-mimicking phantoms. Quantitative evaluation of the reconstructed structures was done by comparing the method output to the real volumes. Comparison was also done with classical PNN and VNN algorithms.

RESULTS

With the fuzzy model quantification errors were less than 4.3%, whereas with classical algorithms, errors were larger (10.3% using PNN, 17.2% using VNN). Furthermore, for very small structures (0.5 cm(3)), errors reached 24.3% using the classical VNN algorithm, while they were about 9.6% with the fuzzy VNN model.

CONCLUSION

These experiments prove that the fuzzy model allows volumes to be determined with better accuracy and reproducibility, especially for small structures (<3 cm(3)).

Towards an accurate and robust method based on fuzzy logic principles for the reconstruction and quantification of large volumes from MR and CT images

The authors have previously evaluated a new method of volume reconstruction and quantification from MR images, based on fuzzy logic (FL) principles. The technique is evaluated here for larger and more complex structures by investigating its accuracy and robustness using MR and CT images. Four large (50-71 cm(3)) and complex (e.g. mimicking a prostate) structures were created and imaged on MR and CT scanners, both with increasing slice thickness. Contours were delineated to generate 112 volumes. MR and CT images were processed using the FL method and a "classical" method of reconstruction on research software. In addition, the CT images were also processed on commercial virtual simulation software. Calculated volumes were compared with actual volumes. The mean +/- standard deviation of the relative variations in calculated target volume using the FL method was found to be 4.4%+/-2.8%, whereas with the "classical" method it was 23.7%+/-6% from axial MR images and 23.3%+/-9.8% from CT images. With the "classical" method, the relative variations in calculated volumes rise with increasing slice thickness, and the displayed volumes show deformations in the longitudinal direction. With the FL method, the volume calculation is not sensitive to the slice thickness and so the deformations are minimal. When used with MR images, our FL method of volume reconstruction is accurate and robust with respect to changes in slice thickness. For CT images, the results are encouraging but some work is still needed to improve the accuracy of the FL method.

An easy-to-use phantom and protocol for weekly PET quality assessment: a multicenter study

The authors have developed a simple phantom and dedicated software for the quality assessment of positron emission tomography (PET) scanners. The phantom is a parallelepiped box filled with a relatively low activity 18FDG solution and in which simple test objects are placed. Various image quality parameters are checked, including signal-to-noise ratio, image uniformity, slice thickness, slice sensitivity profile, spatial resolution, and dose calibration accuracy. Automatic image analysis consists in detecting surfaces and objects, defining regions of interest, acquiring reference point coordinates, and establishing gray-scale profiles. The total time needed for quality assessment (preparation and image acquisition) is less than 15 min with 37 MBq (1 mCi) 18FDG. The system's ease of use encourages frequent image quality assessment-for example, the comparison of PET scanners in interdepartment studies and the monitoring and evaluation of possible drifts over time. By way of an example, the authors present weekly quality assessment results obtained over up to 7 months at four PET facilities.

A new method based on both fuzzy set and possibility theories for tumor volume segmentation on PET images

A new automatic method for tumor volume segmentation on PET images has been developed. The method introduced in this paper is based on previous works in MRA segmentation and involves both fuzzy set and possibility theories. Visual results prove the method efficiency which is confirmed by obtained Jaccard index.

From MIP image to MRA segmentation using fuzzy set theory

The aim of this paper is to describe a semi-automatic method of segmentation in magnetic resonance angiography (MRA). This method, based on fuzzy set theory, uses the information (gray levels) contained in the maximum intensity projection (MIP) image to segment the 3D vascular structure from slices. Tests have been carried out on vascular phantom and on clinical MRA images. This 3D segmentation method has proved to be satisfactory for the detection of vascular structures even for very complex shapes. Finally, this MIP-based approach is semi-automatic and produces a robust segmentation thanks to the contrast-to-noise ratio and to the slice profile which are taken into account to determine the membership of a voxel to the vascular structure.

A method to register intra-treatment ultrasound images to pre-treatment images of prostate

This paper describes a method to register ultrasound images (US) to pre-treatment images. The aim of the work is the information transfer between the pre-treatment imaging modality (MR or CT) and the intra-treatment imaging (US). Ultrasound images are spatially tracked by a stereo-vision system and the prostate boundaries are automatically extracted using a method that combines morphological and adaptive speckle suppression and a priori knowledge. MR/CT images are merged to construct a volume of pelvis using fuzzy logic algorithm and an MPR virtual slice corresponding to the orientation of the US image is generated from the volume. The prostate is segmented from the slice by a model-based method and rigidly registered by ICP algorithm to the US contour. Preliminary experiences gave satisfactory results with short computing time.

Lung liquid clearance in newborn lamb: MRI methods and preliminary results

To study the lung water clearance in vivo at the time of the birth, MR experiments were conducted on newborn lamb immediately after uterine incision deliverance. Images obtained with a fast spin echo sequence enable to quantify lung liquid each 5 minutes during 30 minutes then each 10 minutes for 1.5 hour. From the lung contours, pulmonary volume, pulmonary water, and spatial gradient of pulmonary water were studied. At 2 hours of life, the total pulmonary water content was still high and the liquid clearance was slower in the lower part of the lung. Air inflation increased the size of the distal airways and shifted liquid from the lung lumen towards the pulmonary interstitial tissue. The pulmonary water wash-out was belated and the passage to the aerial life was performed by progressive liberation of the superior pulmonary spaces, water flowing out by gravity toward the lower spaces.

A biomechanical model of the pelvic cavity: first steps

The surgical treatments of genital prolapsus are still empirical and poorly valued. We plan to achieve a method for the objective assessment of the troubles of the pelvic statics and of the various surgical strategies. The methodology considers 3 axes: building a 3D mechanical model of the pelvic cavity, mechanical characterization of the concerned organic tissues, in-vivo measurement of the intravaginal pressures. The MRI images are acquired according 3 orientations (sagittal, axial and coronal); the organs (bladder, vagina and rectum) are manually outlined and their 3D shape is then rebuilt. Uniaxial tensile tests are initially developed on animal samples before being applied to peroperative human samples. In-vivo measurements of the intravaginal pressures are made thanks to a set of 8 strain gauges specially designed. We show results obtained on 2 patients with a different rank of prolapsus. These first steps will be continued by a more detailed geometry of the organs and a characterization of healthy tissues. Dynamic behaviour of the mechanical stresses will also be investigated.

A system for brain tumor volume estimation via MR imaging and fuzzy connectedness

This paper presents a method for the precise, accurate and efficient quantification of brain tumor (glioblastomas) via MRI that can be used routinely in the clinic. Tumor volume is considered useful in evaluating disease progression and response to therapy, and in assessing the need for changes in treatment plans. We use multiple MRI protocols including FLAIR, T1, and T1 with Gd enhancement to gather information about different aspects of the tumor and its vicinity. These include enhancing tissue, nonenhancing tumor, edema, and combinations of edema and tumor. We have adapted the fuzzy connectedness framework for tumor segmentation in this work and the method requires only limited user interaction in routine clinical use. The system has been tested for its precision, accuracy, and efficiency, utilizing 10 patient studies. The percent coefficient of variation (% CV) in volume due to operator subjectivity in specifying seeds for fuzzy connectedness segmentation is less than 1%. The mean operator and computer time required per study for estimating the volumes of both edema and enhancing tumor is about 16 min. The software package is designed to run under operator supervision. Delineation has been found to agree with the operators' visual inspection most of the time except in some cases when the tumor is close to the boundary of the brain. In the latter case, the scalp, surgical scar, or orbital contents are included in the delineation, and an operator has to exclude this manually. The methodology is rapid, robust, consistent, yielding highly reproducible measurements, and is likely to become part of the routine evaluation of brain tumor patients in our health system.

MRI definition of target volumes using fuzzy logic method for three-dimensional conformal radiation therapy

PURPOSE

Three-dimensional (3D) volume determination is one of the most important problems in conformal radiation therapy. Techniques of volume determination from tomographic medical imaging are usually based on two-dimensional (2D) contour definition with the result dependent on the segmentation method used, as well as on the user's manual procedure. The goal of this work is to describe and evaluate a new method that reduces the inaccuracies generally observed in the 2D contour definition and 3D volume reconstruction process.

METHODS AND MATERIALS

This new method has been developed by integrating the fuzziness in the 3D volume definition. It first defines semiautomatically a minimal 2D contour on each slice that definitely contains the volume and a maximal 2D contour that definitely does not contain the volume. The fuzziness region in between is processed using possibility functions in possibility theory. A volume of voxels, including the membership degree to the target volume, is then created on each slice axis, taking into account the slice position and slice profile. A resulting fuzzy volume is obtained after data fusion between multiorientation slices. Different studies have been designed to evaluate and compare this new method of target volume reconstruction and a classical reconstruction method. First, target definition accuracy and robustness were studied on phantom targets. Second, intra- and interobserver variations were studied on radiosurgery clinical cases.

RESULTS

The absolute volume errors are less than or equal to 1.5% for phantom volumes calculated by the fuzzy logic method, whereas the values obtained with the classical method are much larger than the actual volumes (absolute volume errors up to 72%). With increasing MRI slice thickness (1 mm to 8 mm), the phantom volumes calculated by the classical method are increasing exponentially with a maximum absolute error up to 300%. In contrast, the absolute volume errors are less than 12% for phantom volumes calculated by the fuzzy logic method. On radiosurgery clinical cases, target volumes defined by the fuzzy logic method are about half of the size of volumes defined by the classical method. Also, intra- and interobserver variations slightly decrease with the fuzzy logic method, resulting in the definition of a better common volume fraction.

CONCLUSION

Our fuzzy logic method shows accurate, robust, and reproducible results on phantoms and clinical targets imaged on MRI.

Conformal radiotherapy optimization with micromultileaf collimators: comparison with radiosurgery techniques

PURPOSE

Conformal radiotherapy (CRT) consists of irradiating the target volume while avoiding the healthy peripheral tissues and organs at risk as far as possible. One technique used to treat intracranial tumors consists of using micromultileaf collimators (MMLCs). Given the dose constraints involved, it is of interest to optimize MMLC irradiation parameters and compare the results of this technique with those of conventional radiosurgery (RT) techniques (Gamma Knife and linear accelerator stereotactic RT).

METHODS AND MATERIALS

MMLC protocols are optimized in two stages. The orientation of the fields, delimited by a beam's eye view technique, is determined using a genetic algorithm method. The weighting of the fields and subfields when using intensity modulation and the position of the leaves are optimized using a simulated annealing method. We compared the results obtained for 8 clinical cases using 5 intensity-modulated fields with those obtained using the two radiosurgery techniques. The comparison indexes are those defined by the Radiation Therapy Oncology Group (RTOG).

RESULTS

The results of this study demonstrated the advantages of using intensity modulation and the improvement obtained for the RTOG indexes in the case of CRT with MMLC, although the healthy peripheral tissues were less exposed to radiation with the radiosurgery techniques. The results also highlight the difficulty encountered with radiosurgery techniques in obtaining satisfactory dose homogeneity when the protocol is defined with numerous iosocenters.

CONCLUSION

In CRT with MMLC, intensity modulation makes it possible to reduce the number of fields used. It is especially useful to optimize the orientations in the case of target volumes of complex shape or when volumes at risk are in the vicinity of the target. If used correctly, MMLC can be a valuable alternative to conventional radiosurgery techniques.