Comparison of supervised MRI segmentation methods for tumor volume determination during therapy

Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review)

In translational animal study aimed at evaluation of the effectiveness of innovative methods for treating cerebral stroke, including regenerative cell technologies, of particular importance is evaluation of the dynamics of changes in the volume of the cerebral infarction in response to therapy. Among the methods for assessing the focus of infarction, MRI is the most effective and convenient tool for use in preclinical studies. This review provides a description of MR pulse sequences used to visualize cerebral ischemia at various stages of its development, and a detailed description of the MR semiotics of cerebral infarction. A comparison of various methods for morphometric analysis of the focus of a cerebral infarction, including systems based on artificial intelligence for a more objective measurement of the volume of the lesion, is also presented.

Volumetric analysis of IDH-mutant lower-grade glioma: a natural history study of tumor growth rates before and after treatment

BACKGROUND

Lower-grade gliomas (LGGs) with isocitrate dehydrogenase 1 and/or 2 (IDH1/2) mutations have long survival times, making evaluation of treatment efficacy difficult. We investigated the volumetric growth rate of IDH mutant gliomas before and after treatment with established glioma therapies to determine whether a significant change in growth rate could be documented and perhaps be used in the future to evaluate treatment response to investigational agents in LGG trials.

METHODS

In this multicenter retrospective study, 230 adult patients with IDH1/2 mutated LGGs (World Health Organization grade II or III) undergoing surgery, radiation, or chemotherapy for progressive non-enhancing tumor were identified. Subjects were required to have 3 MRI scans containing T2/fluid attenuated inversion recovery imaging spanning a minimum of 6 months prior to treatment. A mixed-effect model was used to estimate tumor growth prior to treatment. A subset of 95 patients who received chemotherapy, radiotherapy, or chemoradiotherapy and had 2 posttreatment imaging time points available were evaluated for change in pre- and posttreatment volumetric growth rates using a piecewise mixed model.

RESULTS

The pretreatment volumetric growth rate across all 230 patients was 27.37%/180 days (95% CI: [23.36%, 31.51%]). In the 95 patients with both pre- and posttreatment scans available, there was a significant difference in volumetric growth rates before (26.63%/180 days, 95% CI: [19.31%, 34.40%]) and after treatment (-15.24% /180 days, 95% CI: [-21.37%, -8.62%]) (P < 0.0001). The growth rates for patient subgroup with 1p/19q codeletion (N = 118) was significantly slower than the rate of the 1p/19q non-codeleted group (N = 68) (22.84% vs 35.49%, P = 0.0108).

CONCLUSION

In this study, we evaluated the growth rates of IDH mutant gliomas before and after standard therapy. Further study is needed to establish whether a change in growth rate is associated with patient survival and its use as a surrogate endpoint in clinical trials for IDH mutant LGGs.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Segmentation of malignant gliomas through remote collaboration and statistical fusion

PURPOSE

Malignant gliomas represent an aggressive class of central nervous system neoplasms. Correlation of interventional outcomes with tumor morphometry data necessitates 3D segmentation of tumors (typically based on magnetic resonance imaging). Expert delineation is the long-held gold standard for tumor segmentation, but is exceptionally resource intensive and subject to intrarater and inter-rater variability. Automated tumor segmentation algorithms have been demonstrated for a variety of imaging modalities and tumor phenotypes, but translation of these methods across clinical study designs is problematic given variation in image acquisition, tumor characteristics, segmentation objectives, and validation criteria. Herein, the authors demonstrate an alternative approach for high-throughput tumor segmentation using Internet-based, collaborative labeling.

METHODS

In a study of 85 human raters and 98 tumor patients, raters were recruited from a general university campus population (i.e., no specific medical knowledge), given minimal training, and provided web-based tools to label MRI images based on 2D cross sections. The labeling goal was characterized as to extract the enhanced tumor cores on T1-weighted MRI and the bright abnormality on T2-weighted MRI. An experienced rater manually constructed the ground truth volumes of a randomly sampled subcohort of 48 tumor subjects (for both T1w and T2w). Raters' taskwise individual observations, as well as the volume wise truth estimates via statistical fusion method, were evaluated over the subjects having the ground truth.

RESULTS

Individual raters were able to reliably characterize (with >0.8 dice similarity coefficient, DSC) the gadolinium-enhancing cores and extent of the edematous areas only slightly more than half of the time. Yet, human raters were efficient in terms of providing these highly variable segmentations (less than 20 s per slice). When statistical fusion was used to combine the results of seven raters per slice for all slices in the datasets, the 3D agreement of the fused results with expertly delineated segmentations was on par with the inter-rater reliability observed between experienced raters using traditional 3D tools (approximately 0.85 DSC). The cumulative time spent per tumor patient with the collaborative approach was equivalent to that with an experienced rater, but the collaborative approach could be achieved with less training time, fewer resources, and efficient parallelization.

CONCLUSIONS

Hence, collaborative labeling is a promising technique with potentially wide applicability to cost-effective manual labeling of medical images.

Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation

RATIONALE AND OBJECTIVES

Quantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients.

MATERIALS AND METHODS

Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input.

RESULTS

Twenty-six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface.

CONCLUSION

Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.

Semi-Supervised Point Prototype Clustering

This paper describes a class of models we call semi-supervised clustering. Algorithms in this category are clustering methods that use information possessed by labeled training data Xd⊂ ℜp as well as structural information that resides in the unlabeled data Xu⊂ ℜp. The labels are used in conjunction with the unlabeled data to help clustering algorithms partition Xu ⊂ ℜp which then terminate without the capability to label other points in ℜp. This is very different from supervised learning, wherein the training data subsequently endow a classifier with the ability to label every point in ℜp. The methodology is applicable in domains such as image segmentation, where users may have a small set of labeled data, and can use it to semi-supervise classification of the remaining pixels in a single image. The model can be used with many different point prototype clustering algorithms. We illustrate how to attach it to a particular algorithm (fuzzy c-means). Then we give two numerical examples to show that it overcomes the failure of many point prototype clustering schemes when confronted with data that possess overlapping and/or non uniformly distributed clusters. Finally, the new method compares favorably to the fully supervised k nearest neighbor rule when applied to the Iris data.

Longitudinal volume analysis from computed tomography: Reproducibility using adrenal glands as surrogate tumors

This study aims to determine the precision (reproducibility) of volume assessment in routine clinical computed tomography (CT) using adrenal glands as surrogate tumors. Seven patients at our institution were identified retrospectively as having received numerous abdominal CT scans (average 13.1, range 5 to 20). The adrenal glands were used as surrogate tumors, assuming no actual volume change. Left and right adrenal gland volumes were assessed by hand segmentation for each patient scan. Over 1240 regions of interest were outlined in total. The reproducibility, expressed as the coefficient of variation (COV), was used to characterize measurement precision. The average volumes were 5.9 and 4.5 cm³ for the left and right adrenal gland, respectively, with COVs of 17.8% and 18.9%, respectively. Using one patient’s data (20 scans) as an example surrogate for a spherical tumor, it was calculated that a 13% change in volume (4.2% change in diameter) could be determined with statistical significance at P=0.05. For this case, cursor positioning error in linear measurement of object size, by even 1 pixel on the CT image, corresponded to a significant change in volume (P=0.05). The precision of volume determination was dependent on total volume. Precision improved with increasing object size (r² =0.367). Given the small dimensions of the adrenal glands, the ~18% COV is likely to be a high estimate compared to larger tumors. Modern CT scanners working with thinner sections (i.e. <1 mm) are likely to produce better measurement precision. The use of volume measurement to quantify changing tumor size is supported as a more precise metric than linear measurement.

Measurement accuracy and reproducibility of semiautomated metric and volumetric lymph node analysis in MDCT

OBJECTIVE

The purpose of this study was to assess the measurement accuracy and reproducibility of semiautomated metric and volumetric lymph node analysis in MDCT.

MATERIALS AND METHODS

Whole-body CT with IV contrast administration was performed on 112 patients. Peripheral (cervical, axillary, and inguinal), abdominal, and thoracic lymph nodes were evaluated independently by two radiologists both manually and with semiautomated segmentation software. Long-axis diameter, short-axis diameter, and volume were measured. Agreement between the semiautomated and manual measurements (measurement error), need for manual correction, and relative interobserver differences were determined. Statistical analysis encompassed the variance inhomogeneity test, intraclass correlation coefficients, and Bland-Altman plots.

RESULTS

In total, 742 peripheral (cervical, axillary, and inguinal), abdominal, and thoracic lymph nodes (mean diameter, 13.2 ± 4.3 mm; range, 4-37 mm) were evaluated. Semiautomatic segmentation without need for further correction was possible for 480 of 742 lymph nodes (64.7%). Calculation of intraclass correlation coefficients revealed high correlation between manual and semiautomatic measurements (r = 0.70-0.81) with a slight trend toward size overestimation for semiautomatic short-axis diameter (14.3%; limits of agreement, -34.3%, 62.9%) and long-axis diameter (11.7%; limits of agreement, -25.2%, 48.5%). Bland-Altman plots showed significantly (p < 0.0001) lower interobserver differences for semiautomated short-axis diameter (1.2%; 95% CI, -39.9% to 42.3%) compared with the manual measurement (7.6%; 95% CI, -38.7% to 53.9%). Among all locations, the relative interobserver difference for semiautomatic volume (2.9%; 95% CI, -31.4% to 37.3%) was significantly lower than that for manual short-axis diameter (p < 0.0001), manual long-axis diameter (0.0178), and semiautomatic short-axis diameter (p < 0.0001).

CONCLUSION

Semiautomatic short-axis diameter, particularly volume measurements, of lymph nodes are, irrespective of location, precise in terms of reproducibility and appear to be considerably more reliable than manual lymph node assessment.

The brain MR Image segmentation techniques and use of diagnostic packages

RATIONALE AND OBJECTIVES

This article provides a survey of segmentation methods for medical images. Usually, classification of segmentation methods is done based on the approaches adopted and the domain of application.

MATERIALS AND METHODS

This survey is conducted on the recent segmentation methods used in biomedical image processing and explores the methods useful for better segmentation. A critical appraisal of the current status of semiautomated and automated methods is made for the segmentation of anatomical medical images emphasizing the advantages and disadvantages. Computer-aided diagnosis (CAD) used by radiologists as a second opinion has become one of the major research areas in medical imaging and diagnostic radiology. A picture archiving communication system (PACS) is an integrated workflow system for managing images and related data that is designed to streamline operations throughout the whole patient care delivery process.

RESULTS

By using PACS, the medical image interpretation may be changed from conventional hard-copy images to soft-copy studies viewed on the systems workstations.

CONCLUSION

The automatic segmentations assist the doctors in making quick diagnosis. The CAD need not be comparable to that of physicians, but is surely complementary.

PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.

Reliability of tumor volume estimation from MR images in patients with malignant glioma. Results from the American College of Radiology Imaging Network (ACRIN) 6662 Trial

Reliable assessment of tumor growth in malignant glioma poses a common problem both clinically and when studying novel therapeutic agents. We aimed to evaluate two software-systems in their ability to estimate volume change of tumor and/or edema on magnetic resonance (MR) images of malignant gliomas. Twenty patients with malignant glioma were included from different sites. Serial post-operative MR images were assessed with two software systems representative of the two fundamental segmentation methods, single-image fuzzy analysis (3DVIEWNIX-TV) and multi-spectral-image analysis (Eigentool), and with a manual method by 16 independent readers (eight MR-certified technologists, four neuroradiology fellows, four neuroradiologists). Enhancing tumor volume and tumor volume plus edema were assessed independently by each reader. Intraclass correlation coefficients (ICCs), variance components, and prediction intervals were estimated. There were no significant differences in the average tumor volume change over time between the software systems (p > 0.05). Both software systems were much more reliable and yielded smaller prediction intervals than manual measurements. No significant differences were observed between the volume changes determined by fellows/neuroradiologists or technologists.Semi-automated software systems are reliable tools to serve as outcome parameters in clinical studies and the basis for therapeutic decision-making for malignant gliomas, whereas manual measurements are less reliable and should not be the basis for clinical or research outcome studies.

Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning

The purpose of this study was to design the steps necessary to create a tumor volume outline from the results of two automated multispectral magnetic resonance imaging segmentation methods and integrate these contours into radiation therapy treatment planning. Algorithms were developed to create a closed, smooth contour that encompassed the tumor pixels resulting from two automated segmentation methods: k-nearest neighbors and knowledge guided. These included an automatic three-dimensional (3D) expansion of the results to compensate for their undersegmentation and match the extended contouring technique used in practice by radiation oncologists. Each resulting radiation treatment plan generated from the automated segmentation and from the outlining by two radiation oncologists for 11 brain tumor patients was compared against the volume and treatment plan from an expert radiation oncologist who served as the control. As part of this analysis, a quantitative and qualitative evaluation mechanism was developed to aid in this comparison. It was found that the expert physician reference volume was irradiated within the same level of conformity when using the plans generated from the contours of the segmentation methods. In addition, any uncertainty in the identification of the actual gross tumor volume by the segmentation methods, as identified by previous research into this area, had small effects when used to generate 3D radiation therapy treatment planning due to the averaging process in the generation of margins used in defining a planning target volume.

Implementation of high-dimensional feature map for segmentation of MR images

A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (> or =3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.

Target Definition in Prostate, Head, and Neck

Target definition is a major source of errors in both prostate and head and neck external-beam radiation treatment. Delineation errors remain constant during the course of radiation and therefore have a large impact on the dose to the tumor. Major sources of delineation variation are visibility of the target including its extensions, disagreement on the target extension, and interpretation or lack of delineation protocols. The visibility of the target can be greatly improved with the use of multimodality imaging. Both in the head and neck and the prostate, computed tomography (CT)-magnetic resonance imaging coregistration decreases the target volume and its variability. CT-positron emission tomography delineation is promising for delineation in head and neck cancer. Despite the better visibility, a different interpretation of the target extension remains a major source of error. The use of coregistration of CT with a second modality, together with improved guidelines for delineation and an online anatomical atlas, increases agreement between observers in prostate, lung, and nasopharynx tumors. Delineation errors should not be treated differently from other geometrical errors. Similar margin recipes for the correction of setup errors and organ motion should be adapted to incorporate the effect of delineation errors. A calculation of a 3-dimensional clinical target volume-planning target volume margin incorporating delineation errors for the head and neck is around 6.1 to 9.7 mm. Given the good local control of IMRT with smaller margins and smaller pathological specimens, it is likely that the delineated CTV frequently overestimates the actual volume.

Radiologic assessment of rectosigmoid cancer before and after neoadjuvant radiation therapy: comparison between quantitation techniques

OBJECTIVE

Volumetric analysis was compared with conventional unidimensional measurements for follow-up of rectosigmoid cancer before and after radiation therapy.

SUBJECTS AND METHODS

Fifteen patients with rectosigmoid cancer underwent helical CT before and after neoadjuvant radiation therapy. The helical CT examination was performed after colon distention with air and IV administration of an antiperistaltic drug. Two scans were obtained: one with the patient in the supine position and the other with the patient in the prone position after contrast medium injection. The maximal wall thickness and the volumetric analysis of the tumor were obtained through manual segmentation.

RESULTS

The mean of the differences between the volumetric analysis of the scans obtained before and after radiation therapy was 8.3 +/- 10.3 (SD) mL (-22.7%) (p <0.05). The mean of the differences between the maximal wall thickness of the pre- and post-radiation therapy scans was 3.4 +/- 2.6 mm (-19.1%) (p <0.05). A significant difference was observed between the variation of the maximal wall thickness and the variation of volumetric analysis in pre- and post-radiation therapy scans (p <0.05). The patients could be classified in different response categories depending on the measurement method and on the response criteria.

CONCLUSION

Volumetric analysis of rectosigmoid cancer is feasible. A long-term study is needed to correlate volumetric assessment with patient outcome.

Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm

RATIONALE AND OBJECTIVE

This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures.

MATERIALS AND METHODS

The watershed method is compared with manual delineation with respect to accuracy, repeatability, and efficiency.

RESULTS

In the 20 patients included in this study, the measured volume of the tumors ranged from 2.7 to 81.9 cm(3). A comparison of the tumor volumes measured with watershed segmentation to the volumes measured with manual delineation shows that the two methods are interchangeable according to the Bland and Altman criterion, and thus equally accurate. The repeatability of the watershed method and the manual method are compared by looking at the similarity of the segmented volumes. The similarity for intraobserver and interobserver variability for watershed segmentation is 96.4% and 95.3%, respectively, compared with 93.5% and 90.0% for manual outlining, from which it may be concluded that the watershed method is more repeatable. Moreover, the watershed algorithm is on average three times faster than manual outlining.

CONCLUSION

The watershed method has an accuracy comparable to that of manual delineation and outperforms manual outlining on the criteria of repeatability and efficiency.

Quantification of tumor tissue populations by multispectral analysis

Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K-means (KM) clustering of the apparent diffusion coefficient (ADC), T2, and proton density (M0) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT-11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T2 identified two necrotic subpopulations, and M0 contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 +/- 147 mm(3)) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 +/- 51 mm(3)). In the treatment study, MS viable tumor volume (at day 10) was 77 +/- 67 mm(3) for the Apo2L/TRAIL+CPT-11 group, and was significantly reduced relative to the control group (292 +/- 127 mm(3), P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo.

Nasopharyngeal Carcinoma Tumor Volume Measurement1

Tumor volume was measured in 69 patients with nasopharyngeal carcinoma. On transverse nonenhanced T1-weighted and gadolinium-enhanced T1-weighted magnetic resonance (MR) images, segmentation was performed by means of seed growing and knowledge-based fuzzy clustering methods. Data were compared with those collected with the manual tracing method and analyzed for interoperator variance and interobserver reliability. There was no significant difference between the volumes determined with manual tracing or semiautomated segmentation (P >.05). On the volume level, Pearson correction coefficients were close for both the manual tracing and semiautomated methods. Significant differences in interoperator variance existed between the two methods on the pixel level (P <.05). Compared with manual tracing, the semiautomated method helped reduce interoperator variance and obtain higher interobserver reliability. Findings in the current study validate the use of semiautomated volume measurement methods for nasopharyngeal carcinoma.

Tongue carcinoma: tumor volume measurement

PURPOSE

To validate the semiautomated methods of tongue carcinoma tumor volume measurement by comparing the conventional manual trace method with 2 semiautomated computer methods: seed growing and region deformation.

MATERIALS AND METHODS

The study population consisted of 16 patients with histology-proven tongue carcinoma. Two head-and-neck radiologists independently measured the tumor volume demonstrated on pretreatment T2-weighted magnetic resonance data sets. The tumor volumes were measured using manual tracing and semiautomated seed growing and region deformation algorithm. Data were recorded for analysis of interoperator variance and interobserver reliability at volume and pixel levels.

RESULTS

There was no significant difference between the manually traced volume and semiautomated segmentation volumes for both operators. No significant difference was found in interobserver variance among the 3 methods at volume level. However, there was significant difference between manual tracing and semiautomated segmentation methods in interobserver reliability at pixel level.

CONCLUSION

The semiautomated methods could achieve satisfactory segmentation results. They could also reduce interoperator variance and obtain a higher interobserver reliability. This study validates the use of semiautomated volume measurement methods for tongue carcinoma.

Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation

PURPOSE

To assess the effectiveness of two automated magnetic resonance imaging (MRI) segmentation methods in determining the gross tumor volume (GTV) of brain tumors for use in radiation therapy treatment planning.

METHODS AND MATERIALS

Two automated MRI tumor segmentation methods (supervised k-nearest neighbors [kNN] and automatic knowledge-guided [KG]) were evaluated for their potential as "cyber colleagues." This required an initial determination of the accuracy and variability of radiation oncologists engaged in the manual definition of the GTV in MRI registered with computed tomography images for 11 glioma patients. Three sets of contours were defined for each of these patients by three radiation oncologists. These outlines were compared directly to establish inter- and intraoperator variability among the radiation oncologists. A novel, probabilistic measurement of accuracy was introduced to compare the level of agreement among the automated MRI segmentations. The accuracy was determined by comparing the volumes obtained by the automated segmentation methods with the weighted average volumes prepared by the radiation oncologists.

RESULTS

Intra- and inter-operator variability in outlining was found to be an average of 20% +/- 15% and 28% +/- 12%, respectively. Lowest intraoperator variability was found for the physician who spent the most time producing the contours. The average accuracy of the kNN segmentation method was 56% +/- 6% for all 11 cases, whereas that of the KG method was 52% +/- 7% for 7 of the 11 cases when compared with the physician contours. For the areas of the contours where the oncologists were in substantial agreement (i.e., the center of the tumor volume), the accuracy of kNN and KG was 75% and 72%, respectively. The automated segmentation methods were found to be least accurate in outlining at the edges of the tumor volume.

CONCLUSIONS

The kNN method was able to segment all cases, whereas the KG method was limited to enhancing tumors and gliomas with clear enhancing edges and no cystic formation. Both methods undersegment the tumor volume when compared with the radiation oncologists and performed within the variability of the contouring performed by experienced radiation oncologists based on the same data.

Quantification of tumour response to radiotherapy

In 1979, the World Health Organization (WHO) established criteria based on tumour volume change for classifying response to therapy as (i) progressive disease (PD), (ii) partial recovery (PR), and (iii) no change (NC). Typically, the tumour volume is reported from diameter measurements, using the calliper method. Alternatively, the Cavalieri method provides unbiased volume estimates of any structure without assumptions about its shape. In this study, we applied the Cavalieri method in combination with point counting to investigate the changes in tumour volume in four patients with high grade glioma, using 3D MRI. In particular, the volume of tumour within the enhancement boundary, the enhancing abnormality (EA), was estimated from T(1) weighted images, and the volume of the non-enhancing abnormality, (NEA) enhancing abnormality, was estimated from T(2) relaxation time and magnetic transfer ratio tissue characterization maps. We compared changes in tumour volume estimated by the Cavalieri method with those obtained using the calliper method. Absolute tumour volume differed significantly between the two methods. Analysis of relative change in tumour volume, based on the WHO criteria, provided a different classification using the calliper and Cavalieri methods. The benefit of the Cavalieri method over the calliper method in the estimation of tumour volume is justified by the following factors. First, Cavalieri volume estimates are mathematically unbiased. Second, the Cavalieri method is highly efficient under an appropriate sampling density (i.e. EA volume estimates can be obtained with a coefficient of error no higher than 5% in 2-3 min). Third, the source of variation of the volume estimates due to disagreements between observers, and within observer, is much greater in the positioning of the calliper diameters than in the identification of the tumour boundaries when applying the Cavalieri method. Additionally, the error prediction formula, available to estimate the coefficient of error of Cavalieri volume estimates from the data, allows us to establish more precise classification criteria against which to identify potentially clinical significant changes in tumour volume.

Improved detection of metastases on magnetic resonance images by digital tissue recognition: validation using VX-2 tumor in the rabbit

PURPOSE

To evaluate the ability of a prototype digital tissue recognition (DTR) system to improve the accuracy of detection of metastases on magnetic resonance (MR) images in the rabbit VX-2 tumor model.

MATERIALS AND METHODS

Multiple MR imaging (MRI) sequences, including pre-contrast and post-contrast enhanced T1-weighted, T2-weighted, proton-density, and fast short inversion time inversion recovery (FSTIR), were acquired for six rabbits implanted with VX-2 adenocarcinoma. For each rabbit, DTR used the MR intensity characteristics of a known tumor site to highlight other areas suspicious for tumor. Three independent veterinary radiologists with extensive experience in animal MRI interpreted the images for tumor both without and with the results of DTR. The conventional and DTR-assisted interpretations were compared to pathology.

RESULTS

Using DTR, the radiologists found an average of 13.2% more true positive sites with a 10.3% reduction in false positives compared to unassisted interpretation. The improvement for the radiologists was statistically significant (McNemar's test, P = 0.0004). The agreement between radiologists using DTR was consistently higher than for their conventional interpretations (kappa statistic).

CONCLUSION

Compared with conventional interpretation of MR images, the use of DTR provided a statistically significant improvement in the accuracy of locating more and smaller sites of tumor. This improvement was achieved without the benefit of post-contrast images.

Angiogenesis in glioma: molecular mechanisms and roadblocks to translation

Gliomas are characterized by very high levels of neo-vascularization holding out the hope that therapies aimed at angiogenesis will have a significant impact on this intractable family of tumors. Intense research into the molecular mechanisms that drive the formation of new blood vessels in response to tumor growth has revealed a great deal of complexity, at the heart of which are competing pro- and anti-angiogenic influences. The relevant signaling pathways, and how they might be manipulated to interfere in the promotion of vessel growth are discussed. Several types of anti-angiogenic lead compounds are already in clinical trials, but assessing their impact on brain tumors is not straightforward. We discuss in depth some of the practical aspects of using imaging to more meaningfully follow tumor progression and response to treatment, which is particularly relevant to the use of therapies that target blood flow directly, which is fundamental to modern imaging modalities.

Current methods in medical image segmentation

Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.

Brain tumor volume measurement: comparison of manual and semiautomated methods

PURPOSE

To compare the reliability of two approaches to measuring enhancing brain tumor volumes--the conventional manual trace method and a threshold-based, semiautomated computer software method.

MATERIALS AND METHODS

Two operators rated contrast material-enhanced, T1-weighted axial magnetic resonance (MR) image data sets from 16 patients aged 21-71 years with high-grade gliomas. Each MR data set was rated twice by using manual tracing and twice by using the semiautomated method. The semiautomated measurement method involved a thresholding algorithm based on mixture modeling. The data collection time for each method was recorded. Reliability was measured by using inter- and intraoperator agreement indexes.

RESULTS

Mean intraoperator agreement indexes (+/- SD) were 0.90 +/- 0.09 (operator 1) and 0.83 +/- 0.15 (operator 2) for the manual trace method and 0.83 +/- 0.17 (operator 1) and 0.84 +/- 0.16 (operator 2) for the semiautomated measurement method. The mean interoperator agreement was 0.85 +/- 0.14 for the manual method and 0.82 +/- 0.18 for the semiautomated method. The semiautomated method was faster than the manual trace method by an average of 4.6 minutes per patient.

CONCLUSION

The semiautomated computer method of measuring tumor volume was faster than the manual trace method. Semiautomated computer approaches offer an alternative to manual tracing for measuring serial tumor volumes in patients with high-grade brain neoplasms.

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images

Methods for brain tissue classification or segmentation of structural magnetic resonance imaging (MRI) data should ideally be independent of human operators for reasons of reliability and tractability. An algorithm is described for fully automated segmentation of dual echo, fast spin-echo MRI data. The method is used to assign fuzzy-membership values for each of four tissue classes (gray matter, white matter, cerebrospinal fluid and dura) to each voxel based on partition of a two dimensional feature space. Fuzzy clustering is modified for this application in two ways. First, a two component normal mixture model is initially fitted to the thresholded feature space to identify exemplary gray and white matter voxels. These exemplary data protect subsequently estimated cluster means against the tendency of unmodified fuzzy clustering to equalize the number of voxels in each class. Second, fuzzy clustering is implemented in a moving window scheme that accommodates reduced image contrast at the axial extremes of the transmitting/receiving coil. MRI data acquired from 5 normal volunteers were used to identify stable values for three arbitrary parameters of the algorithm: feature space threshold, relative weight of exemplary gray and white matter voxels, and moving window size. The modified algorithm incorporating these parameter values was then used to classify data from simulated images of the brain, validating the use of fuzzy-membership values as estimates of partial volume. Gray:white matter ratios were estimated from 20 twenty normal volunteers (mean age 32.8 years). Processing time for each three-dimensional image was approximately 30 min on a 170 MHz workstation. Mean cerebral gray and white matter volumes estimated from these automatically segmented images were very similar to comparable results previously obtained by operator dependent methods, but without their inherent unreliability.

Role of the inflamed synovial volume of the wrist in defining remission of rheumatoid arthritis with gadolinium-enhanced 3D-SPGR MR imaging

The purpose of this study was to assess the role of inflamed synovial volume (ISV) in defining a state of remission in rheumatoid arthritis (RA) with contrast-enhanced, fat-suppression, three-dimensional (3D) gradient-recalled acquisition in the steady state with radiofrequency spoiling (SPGR) magnetic resonance (MR) imaging. Sixteen patients with RA (5 remission and 11 non-remission patients) were enrolled in this study. Contrast-enhanced, fat-suppression, 3D-SPGR MR imaging was performed before (n = 12) and after (n = 16) a mean 17 months of disease-modifying antirheumatic drugs (DMARDs). ISV was calculated by using a segmentation method. Statistical analysis of changes in ISVs and residual ISVs between the remission and the non-remission groups was performed. Intra- and inter-observer reproducibility was tested. Residual ISVs and relative changes in ISVs were 3.23 +/- 1.84 cm(3) and 51.4% (range 47.6-55.2%) in the remission group and 6.26 +/- 2. 03 cm(3)and 31.4% (range -73.5-53.5%) in the non-remission group. Both values were significantly different between the two groups (P < 0.05 and 0.05, respectively). Volume measurement showed high reproducibility: Intra- and inter-observer mean percentage errors were 5.04, 7.06, and 5.09%, respectively. Residual ISVs and relative changes in ISVs measured by MR imaging may provide objective and quantitative parameters in defining a state of remission in RA after therapy; however, the clinical utility of these measurements remains to be verified. J. Magn. Reson. Imaging 1999;10:202-208.

Usability of semiautomatic segmentation algorithms for tumor volume determination

RATIONALE AND OBJECTIVES

Tumor volume is an important parameter for clinical decision making. At present, semiautomatic image segmentation is not a standard for tumor volumetry. The aim of this work was to investigate the usability of semiautomatic algorithms for tumor volume determination.

METHODS

Semiautomatic region- and volume-growing, isocontour, snakes, hierarchical, and histogram-based segmentation algorithms were tested for accuracy, contour variability, and time performance. The test were performed on a newly developed organic phantom for the simulation of a human liver and liver metastases. The real tumor volumes were measured by water displacement. These measured volumes were used as the gold standard for determining the accuracy of the algorithms.

RESULTS

Variability of the segmented volumes ranging from 3.9 +/- 3.2% (isocontour algorithm) to 11.5 +/- 13.9% (hierarchical segmentation) was observed. The segmentation time per slice varied between 32 (volume-growing) and 72 seconds (snakes) on an IBM/RS6000 workstation.

CONCLUSIONS

Only the region-growing and isocontour algorithms have the potential to be used for tumor volumetry. However, further improvements of these algorithms are necessary before they can be placed into clinical use.

The differentiation of DSM-III-R psychotic depression in later life from nonpsychotic depression: comparisons of brain changes measured by multispectral analysis of magnetic resonance brain images, neuropsychological findings, and clinical features

BACKGROUND

Psychotic depression has been proposed as a distinct subtype of major depression. There is considerable evidence for this in younger patients, although the neuroimaging has been rudimentary. Volumetric imaging studies are required of consecutive cohorts of patients with depression.

METHODS

Ninety-nine consecutive elderly patients were diagnosed with DSM-III-R major depression. Eighteen were psychotic, and 81 were not. Sixty-six patients were given a neuropsychological test battery, and 44 had a magnetic resonance imaging brain scan. A model integrating clinical, psychological, and neuroimaging findings for the explanation of delusion formation during depression is proposed.

RESULTS

Psychotic depression was characterized by worse physical health, more family history of depression, a poorer response to antidepressant drugs, and more severe lowering of mood; however, the strongest predictors of the presence of delusions were diencephalic atrophy, reticular activating system lesions, brain stem atrophy, and left-sided frontotemporal atrophy. The psychotic patients had poorer performance on tests of frontal lobe function and mental processing speed.

CONCLUSIONS

In the elderly, psychotic depression is etiologically, clinically, and neuroradiologically distinct, and has different treatment requirements, from nonpsychotic major depression.

Review and evaluation of MRI nonuniformity corrections for brain tumor response measurements

Current MRI nonuniformity correction techniques are reviewed and investigated. Many approaches are used to remedy this artifact, but it is not clear which method is the most appropriate in a given situation, as the applications have been with different MRI coils and different clinical applications. In this work four widely used nonuniformity correction techniques are investigated in order to assess the effect on tumor response measurements (change in tumor volume over time): a phantom correction method, an image smoothing technique, homomorphic filtering, and surface fitting approach. Six brain tumor cases with baseline and follow-up MRIs after treatment with varying degrees of difficulty of segmentation were analyzed without and with each of the nonuniformity corrections. Different methods give significantly different correction images, indicating that rf nonuniformity correction is not yet well understood. No improvement in tumor segmentation or in tumor growth/shrinkage assessment was achieved using any of the evaluated corrections.

Magnetic resonance image segmentation using pattern recognition, and applied to image registration and quantitation

This review highlights various magnetic resonance image (MRI) segmentation algorithms that employ pattern recognition. The procedures are grouped into two categories: low- to intermediate-level, and high-level image processing. The former consists of grey level histogram analysis, texture definition, edge identification, region growing, and contour following. The roles of significant prior knowledge, neural networks and cluster analysis are examined by producing objective identification of anatomical structures. The application of the segmented anatomical structures in image registration, to monitor the disease progression or growth of anatomy in normal volunteers and patients, is highlighted. The use of the segmented anatomy in measuring volumes of structures in normals and patients is also examined.

Automatic tumor segmentation using knowledge-based techniques

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation

An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multispectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to "ground truth," (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measurement of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials.

Medical image analysis with fuzzy models

This paper updates several recent surveys on the use of fuzzy models for segmentation and edge detection in medical image data. Our survey is divided into methods based on supervised and unsupervised learning (that is, on whether there are or are not labelled data available for supervising the computations), and is organized first and foremost by groups (that we know of!) that are active in this area. Our review is aimed more towards 'who is doing it' rather than 'how good it is'. This is partially dictated by the fact that direct comparisons of supervised and unsupervised methods is somewhat akin to comparing apples and oranges. There is a further subdivision into methods for two- and three-dimensional data and/or problems. We do not cover methods based on neural-like networks or fuzzy reasoning systems. These topics are covered in a recently published companion survey by keller et al.

Monitoring brain tumor response to therapy using MRI segmentation

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.

Normal brain volume measurements using multispectral MRI segmentation

The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

Magnetic resonance imaging (MRI): considerations and applications in radiotherapy treatment planning

The emerging utilisation of conformal radiotherapy (RT) planning requires sophisticated imaging modalities. Magnetic resonance imaging (MRI) has introduced several added imaging benefits that may confer an advantage over the use of computed tomography (CT) in RT planning such as improved soft tissue definition, unrestricted multiplannar and volumetric imaging as well as physiological and biochemical information with magnetic resonance (MR) angiography and spectroscopy. However, MRI has not yet seriously challenged CT for RT planning in most sites. The reasons for this include: (1) the poor imaging of bone and the lack of electron density information from MRI required for dosimetry calculations; (2) the presence of intrinsic system-related and object-induced MR image distortions; (3) the paucity of widely available computer software to accurately and reliably integrate and manipulate MR images within existing RT planning systems. In this review, the basic principals of MRI with its present potential and limitations for RT planning as well as possible solutions will be examined. Methods of MRI data acquisition and processing including image segmentation and registration to allow its application in RT planning will be discussed. Despite the difficulties listed, MRI has complemented CT-based RT planning and in some regions of the body especially the brain, it has been used alone with some success. Recent work with doped gel compounds allow the MRI mapping of dose distributions thus potentially providing a quality assurance tool and in a manner analogous to CT, the production of dose-response information in the form of dose volume histograms. However, despite the promise of MRI, much development research remains before its full potential and cost-effectiveness can be assessed.

Tumour volume determination from MR images by morphological segmentation

Accurate tumour volume measurement from MR images requires some form of objective image segmentation, and therefore a certain degree of automation. Manual methods of separating data according to the various tissue types which they are thought to represent are inherently prone to operator subjectivity and can be very time consuming. A segmentation procedure based on morphological edge detection and region growing has been implemented and tested on a phantom of known adjustable volume. Comparisons have been made with a traditional data thresholding procedure for the determination of tumour volumes on a set of patients with intracerebral glioma. The two methods are shown to give similar results, with the morphological segmentation procedure having the advantages of being automated and faster.