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 addresse

The role of machine and deep learning in modern medical physics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155454/1/mp14088_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155454/2/mp14088.pd

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

Purpose: Several methods have been proposed for the segmentation of 18F-FDG uptake in PET. In this study, we assessed the performance of four categories of 18F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark. Methods: Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3-T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected "en bloc”, frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference. Results: The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (−5.9 ± 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26-0.72) for the overlap index. Conclusion: The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs defined using the surgical specimens. Adaptive thresholding techniques need to be calibrated for each PET scanner and acquisition/processing protocol, and should not be used without optimizatio

Techniques and software tool for 3D multimodality medical image segmentation

The era of noninvasive diagnostic radiology and image-guided radiotherapy has witnessed burgeoning interest in applying different imaging modalities to stage and localize complex diseases such as atherosclerosis or cancer. It has been observed that using complementary information from multimodality images often significantly improves the robustness and accuracy of target volume definitions in radiotherapy treatment of cancer. In this work, we present techniques and an interactive software tool to support this new framework for 3D multimodality medical image segmentation. To demonstrate this methodology, we have designed and developed a dedicated open source software tool for multimodality image analysis MIASYS. The software tool aims to provide a needed solution for 3D image segmentation by integrating automatic algorithms, manual contouring methods, image preprocessing filters, post-processing procedures, user interactive features and evaluation metrics. The presented methods and the accompanying software tool have been successfully evaluated for different radiation therapy and diagnostic radiology applications

Experimental evaluation of x‐ray acoustic computed tomography for radiotherapy dosimetry applications

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136272/1/mp12039_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136272/2/mp12039.pd

Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data

Combining handcrafted features with latent variables in machine learning for prediction of radiationâ induced lung damage

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/1/mp13497.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/2/mp13497_am.pd

Introduction to machine and deep learning for medical physicists

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155469/1/mp14140_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155469/2/mp14140.pd

A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations

Estimating the proper margins for the planning target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. Developments in image-guidance and the presence of onboard imaging technologies facilitate the process of correcting setup errors. However, estimation of errors to organ motions remain an open question due to the lack of proper software tools to accompany these imaging technological advances. Therefore, we have developed a new tool for visualization and quantification of deformations from daily images. The tool allows for estimation of tumor coverage and normal tissue exposure as a function of selected margin (isotropic or anisotropic). Moreover, the software allows estimation of the optimal margin based on the probability of an organ being present at a particular location. Methods based on swarm intelligence, specifically Ant Colony Optimization (ACO) are used to provide an efficient estimate of the optimal margin extent in each direction. ACO can provide global optimal solutions in highly nonlinear problems such as margin estimation. The proposed method is demonstrated using cases from gastric lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Preliminary results using Dice similarity index are promising and it is expected that the proposed tool will be very helpful and have significant impact for guiding future margin definition protocols

Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data

Radiotherapy outcomes are determined by complex interactions between physical and biological factors, reflecting both treatment conditions and underlying genetics. Recent advances in radiotherapy and biotechnology provide new opportunities and challenges for predicting radiation-induced toxicities, particularly radiation pneumonitis (RP), in lung cancer patients. In this work, we utilize datamining methods based on machine learning to build a predictive model of lung injury by retrospective analysis of treatment planning archives. In addition, biomarkers for this model are extracted from a prospective clinical trial that collects blood serum samples at multiple time points. We utilize a 3-way proteomics methodology to screen for differentially expressed proteins that are related to RP. Our preliminary results demonstrate that kernel methods can capture nonlinear dose-volume interactions, but fail to address missing biological factors. Our proteomics strategy yielded promising protein candidates, but their role in RP as well as their interactions with dose-volume metrics remain to be determined