Developing an understanding of artificial intelligence lung nodule risk prediction using insights from the Brock model.

OBJECTIVES: To determine if predictions of the Lung Cancer Prediction convolutional neural network (LCP-CNN) artificial intelligence (AI) model are analogous to the Brock model. METHODS: In total, 10,485 lung nodules in 4660 participants from the National Lung Screening Trial (NLST) were analysed. Both manual and automated nodule measurements were inputted into the Brock model, and this was compared to LCP-CNN. The performance of an experimental AI model was tested after ablating imaging features in a manner analogous to removing predictors from the Brock model. First, the nodule was ablated leaving lung parenchyma only. Second, a sphere of the same size as the nodule was implanted in the parenchyma. Third, internal texture of both nodule and parenchyma was ablated. RESULTS: Automated axial diameter (AUC 0.883) and automated equivalent spherical diameter (AUC 0.896) significantly improved the accuracy of Brock when compared to manual measurement (AUC 0.873), although not to the level of the LCP-CNN (AUC 0.936). Ablating nodule and parenchyma texture (AUC 0.915) led to a small drop in AI predictive accuracy, as did implanting a sphere of the same size as the nodule (AUC 0.889). Ablating the nodule leaving parenchyma only led to a large drop in AI performance (AUC 0.717). CONCLUSIONS: Feature ablation is a feasible technique for understanding AI model predictions. Nodule size and morphology play the largest role in AI prediction, with nodule internal texture and background parenchyma playing a limited role. This is broadly analogous to the relative importance of morphological factors over clinical factors within the Brock model. KEY POINTS: • Brock lung cancer risk prediction accuracy was significantly improved using automated axial or equivalent spherical measurements of lung nodule diameter, when compared to manual measurements. • Predictive accuracy was further improved by using the Lung Cancer Prediction convolutional neural network, an artificial intelligence-based model which obviates the requirement for nodule measurement. • Nodule size and morphology are important factors in artificial intelligence lung cancer risk prediction, with nodule texture and background parenchyma contributing a small, but measurable, role.

PET motion correction in context of integrated PET/MR: Current techniques, limitations, and future projections.

Patient motion is an important consideration in modern PET image reconstruction. Advances in PET technology mean motion has an increasingly important influence on resulting image quality. Motion-induced artifacts can have adverse effects on clinical outcomes, including missed diagnoses and oversized radiotherapy treatment volumes. This review aims to summarize the wide variety of motion correction techniques available in PET and combined PET/CT and PET/MR, with a focus on the latter. A general framework for the motion correction of PET images is presented, consisting of acquisition, modeling, and correction stages. Methods for measuring, modeling, and correcting motion and associated artifacts, both in literature and commercially available, are presented, and their relative merits are contrasted. Identified limitations of current methods include modeling of aperiodic and/or unpredictable motion, attaining adequate temporal resolution for motion correction in dynamic kinetic modeling acquisitions, and maintaining availability of the MR in PET/MR scans for diagnostic acquisitions. Finally, avenues for future investigation are discussed, with a focus on improvements that could improve PET image quality, and that are practical in the clinical environment.

Identifying relevant biomarkers of brain injury from structural MRI: Validation using automated approaches in children with unilateral cerebral palsy.

Previous studies have proposed that the early elucidation of brain injury from structural Magnetic Resonance Images (sMRI) is critical for the clinical assessment of children with cerebral palsy (CP). Although distinct aetiologies, including cortical maldevelopments, white and grey matter lesions and ventricular enlargement, have been categorised, these injuries are commonly only assessed in a qualitative fashion. As a result, sMRI remains relatively underexploited for clinical assessments, despite its widespread use. In this study, several automated and validated techniques to automatically quantify these three classes of injury were generated in a large cohort of children (n = 139) aged 5-17, including 95 children diagnosed with unilateral CP. Using a feature selection approach on a training data set (n = 97) to find severity of injury biomarkers predictive of clinical function (motor, cognitive, communicative and visual function), cortical shape and regional lesion burden were most often chosen associated with clinical function. Validating the best models on the unseen test data (n = 42), correlation values ranged between 0.545 and 0.795 (p<0.008), indicating significant associations with clinical function. The measured prevalence of injury, including ventricular enlargement (70%), white and grey matter lesions (55%) and cortical malformations (30%), were similar to the prevalence observed in other cohorts of children with unilateral CP. These findings support the early characterisation of injury from sMRI into previously defined aetiologies as part of standard clinical assessment. Furthermore, the strong and significant association between quantifications of injury observed on structural MRI and multiple clinical scores accord with empirically established structure-function relationships.

Design and utilisation of protocols to characterise dynamic PET uptake of two tracers using basis pursuit.

Imaging using more than one biological process using PET could be of great utility, but despite previously proposed approaches to dual-tracer imaging, it is seldom performed. The alternative of performing multiple scans is often infeasible for clinical practice or even in research studies. Dual-tracer PET scanning allows for multiple PET radiotracers to be imaged within the same imaging session. In this paper we describe our approach to utilise the basis pursuit method to aid in the design of dual-tracer PET imaging experiments, and later in separation of the signals. The advantage of this approach is that it does not require a compartment model architecture to be specified or even that both signals are distinguishable in all cases. This means the method for separating dual-tracer signals can be used for many feasible and useful combinations of biology or radiotracer, once an appropriate scanning protocol has been decided upon. Following a demonstration in separating the signals from two consecutively injected radionuclides in a controlled experiment, phantom and list-mode mouse experiments demonstrated the ability to test the feasibility of dual-tracer imaging protocols for multiple injection delays. Increases in variances predicted for kinetic macro-parameters V D and K I in brain and tumoral tissue were obtained when separating the synthetically combined data. These experiments confirmed previous work using other approaches that injections delays of 10-20 min ensured increases in variance were kept minimal for the test tracers used. On this basis, an actual dual-tracer experiment using a 20 min delay was performed using these radio tracers, with the kinetic parameters (V D and K I) extracted for each tracer in agreement with the literature. This study supports previous work that dual-tracer PET imaging can be accomplished provided certain constraints are adhered to. The utilisation of basis pursuit techniques, with its removed need to specify a model architecture, allows the feasibility of a range of imaging protocols to be investigated via simulation in a straight-forward manner for a wide range of possible scenarios. The hope is that the ease of utilising this approach during feasibility studies and in practice removes any perceived technical barrier to performing dual-tracer imaging.

Measuring neuroplasticity associated with cerebral palsy rehabilitation: An MRI based power analysis.

Researchers in the field of child neurology are increasingly looking to supplement clinical trials of motor rehabilitation with neuroimaging in order to better understand the relationship between behavioural training, brain changes, and clinical improvements. Randomised controlled trials are typically accompanied by sample size calculations to detect clinical improvements but, despite the large cost of neuroimaging, not equivalent calculations for concurrently acquired imaging neuroimaging measures of changes in response to intervention. To aid in this regard, a power analysis was conducted for two measures of brain changes that may be indexed in a trial of rehabilitative therapy for cerebral palsy: cortical thickness of the impaired primary sensorimotor cortex, and fractional anisotropy of the impaired, delineated corticospinal tract. Power for measuring fractional anisotropy was assessed for both region-of-interest-seeded and fMRI-seeded diffusion tractography. Taking into account practical limitations, as well as data loss due to behavioural and image-processing issues, estimated required participant numbers were 101, 128 and 59 for cortical thickness, region-of-interest-based tractography, and fMRI-seeded tractography, respectively. These numbers are not adjusted for study attrition. Although these participant numbers may be out of reach of many trials, several options are available to improve statistical power, including careful preparation of participants for scanning using mock simulators, careful consideration of image processing options, and enrolment of as homogeneous a cohort as possible. This work suggests that smaller and moderate sized studies give genuine consideration to harmonising scanning protocols between groups to allow the pooling of data.

Federated optimisation of kinetic analysis problems.

Positron Emission Tomography (PET) data is intrinsically dynamic, and kinetic analysis of dynamic PET data can substantially augment the information provided by static PET reconstructions. Yet despite the insights into disease that kinetic analysis offers, it is not used clinically and seldom used in research beyond the preclinical stage. The utility of PET kinetic analysis is hampered by several factors including spatial inconsistency within regions of homogeneous tissue and relative computational expense when fitting complex models to individual voxels. Even with sophisticated algorithms inconsistencies can arise because local optima frequently have narrow basins of convergence, are surrounded by relatively flat (uninformative) regions, have relatively low-gradient valley floors, or combinations thereof. Based on the observation that cost functions for individual voxels frequently bear some resemblance to each-other, this paper proposes the federated optimisation of the individual kinetic analysis problems within a given image. This approach shares parameters proposed during optimisation with other, similar voxels. Federated optimisation exploits the redundancy typical of large medical images to improve the optimisation residuals, computational efficiency and, to a limited extent, image consistency. This is achieved without restricting the formulation of the kinetic model, resorting to an explicit regularisation parameter, or limiting the resolution at which parameters are computed.

Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy.

Understanding the relationships between the structure and function of the brain largely relies on the qualitative assessment of Magnetic Resonance Images (MRIs) by expert clinicians. Automated analysis systems can support these assessments by providing quantitative measures of brain injury. However, the assessment of deep gray matter structures, which are critical to motor and executive function, remains difficult as a result of large anatomical injuries commonly observed in children with Cerebral Palsy (CP). Hence, this article proposes a robust surrogate marker of the extent of deep gray matter injury based on impingement due to local ventricular enlargement on surrounding anatomy. Local enlargement was computed using a statistical shape model of the lateral ventricles constructed from 44 healthy subjects. Measures of injury on 95 age-matched CP patients were used to train a regression model to predict six clinical measures of function. The robustness of identifying ventricular enlargement was demonstrated by an area under the curve of 0.91 when tested against a dichotomised expert clinical assessment. The measures also showed strong and significant relationships for multiple clinical scores, including: motor function (r2 = 0.62, P < 0.005), executive function (r2 = 0.55, P < 0.005), and communication (r2 = 0.50, P < 0.005), especially compared to using volumes obtained from standard anatomical segmentation approaches. The lack of reliance on accurate anatomical segmentations and its resulting robustness to large anatomical variations is a key feature of the proposed automated approach. This coupled with its strong correlation with clinically meaningful scores, signifies the potential utility to repeatedly assess MRIs for clinicians diagnosing children with CP. Hum Brain Mapp 37:3795-3809, 2016. © 2016 Wiley Periodicals, Inc.

Alterations in regional shape on ipsilateral and contralateral cortex contrast in children with unilateral cerebral palsy and are predictive of multiple outcomes.

Congenital brain lesions result in a wide range of cerebral tissue alterations observed in children with cerebral palsy (CP) that are associated with a range of functional impairments. The relationship between injury severity and functional outcomes, however, remains poorly understood. This research investigates the differences in cortical shape between children with congenital brain lesions and typically developing children (TDC) and investigates the correlations between cortical shape and functional outcome in a large cohort of patients diagnosed with unilateral CP. Using 139 structural magnetic resonance images, including 95 patients with clinically diagnosed CP and 44 TDC, cortical segmentations were obtained using a modified expectation maximization algorithm. Three shape characteristics (cortical thickness, curvature, and sulcal depth) were computed within a number of cortical regions. Significant differences in these shape measures compared to the TDC were observed on both the injured hemisphere of children with CP (P < 0.004), as well as on the apparently uninjured hemisphere, illustrating potential compensatory mechanisms in these children. Furthermore, these shape measures were significantly correlated with several functional outcomes, including motor, cognition, vision, and communication (P < 0.012), with three out of these four models performing well on test set validation. This study highlights that cortical neuroplastic effects may be quantified using MR imaging, allowing morphological changes to be studied longitudinally, including any influence of treatment. Ultimately, such approaches could be used for the long term prediction of outcomes and the tailoring of treatment to individuals. Hum Brain Mapp 37:3588-3603, 2016. © 2016 Wiley Periodicals, Inc.

Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy.

White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs) of children with cerebral palsy (CP). Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established. In this study, we perform automatic lesion segmentation on a large cohort of data (107 children with unilateral CP and 18 healthy children) with a new, validated method for segmenting both white matter (WM) and grey matter (GM) lesions. The method has better accuracy (94%) than the best current methods (73%), and only requires standard structural MRI sequences. Anatomical lesion burdens most predictive of clinical scores of motor, cognitive, visual and communicative function were identified using the Least Absolute Shrinkage and Selection operator (LASSO). The improved segmentations enabled identification of significant correlations between regional lesion burden and clinical performance, which conform to known structure-function relationships. Model performance was validated in an independent test set, with significant correlations observed for both WM and GM regional lesion burden with motor function (p < 0.008), and between WM and GM lesions alone with cognitive and visual function respectively (p < 0.008). The significant correlation of GM lesions with functional outcome highlights the serious implications GM lesions, in addition to WM lesions, have for prognosis, and the utility of structural MRI alone for quantifying lesion burden and planning therapy interventions.

Optimization of MRI-based scoring scales of brain injury severity in children with unilateral cerebral palsy.

BACKGROUND: Several scoring systems for measuring brain injury severity have been developed to standardize the classification of MRI results, which allows for the prediction of functional outcomes to help plan effective interventions for children with cerebral palsy. OBJECTIVE: The aim of this study is to use statistical techniques to optimize the clinical utility of a recently proposed template-based scoring method by weighting individual anatomical scores of injury, while maintaining its simplicity by retaining only a subset of scored anatomical regions. MATERIALS AND METHODS: Seventy-six children with unilateral cerebral palsy were evaluated in terms of upper limb motor function using the Assisting Hand Assessment measure and injuries visible on MRI using a semiquantitative approach. This cohort included 52 children with periventricular white matter injury and 24 with cortical and deep gray matter injuries. A subset of the template-derived cerebral regions was selected using a data-driven region selection algorithm. Linear regression was performed using this subset, with interaction effects excluded. RESULTS: Linear regression improved multiple correlations between MRI-based and Assisting Hand Assessment scores for both periventricular white matter (R squared increased to 0.45 from 0, P < 0.0001) and cortical and deep gray matter (0.84 from 0.44, P < 0.0001) cohorts. In both cohorts, the data-driven approach retained fewer than 8 of the 40 template-derived anatomical regions. CONCLUSION: The equal or better prediction of the clinically meaningful Assisting Hand Assessment measure using fewer anatomical regions highlights the potential of these developments to enable enhanced quantification of injury and prediction of patient motor outcome, while maintaining the clinical expediency of the scoring approach.

The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape modelling approaches to identify anatomical injury. The benefits of automatic segmentation in CP is important as it has the potential to elucidate the underlying relationship between image derived features and patient outcome, enabling better tailoring of therapy to individual patients.

Increasing feasibility and utility of (18)F-FDOPA PET for the management of glioma.

INTRODUCTION: Despite radical treatment therapies, glioma continues to carry with it a uniformly poor prognosis. Patients diagnosed with WHO Grade IV glioma (glioblastomas; GBM) generally succumb within two years, even those with WHO Grade III anaplastic gliomas and WHO Grade II gliomas carry prognoses of 2-5 and 2 years, respectively. PET imaging with (18)F-FDOPA allows in vivo assessment of the metabolism of glioma relative to surrounding tissues. The high sensitivity of (18)F-DOPA imaging grants utility for a number of clinical applications. METHODS: A collection of published work about (18)F-FDOPA PET was made and a critical review was discussed and written. RESULTS: A number of research papers have been published demonstrating that in conjunction with MRI, (18)F-FDOPA PET provides greater sensitivity and specificity than these modalities in detection, grading, prognosis and validation of treatment success in both primary and recurrent gliomas. In further comparisons with (11)C-MET, (18)F-FLT, (18)F-FET and MRI, (18)F-FDOPA has shown similar or better efficacy. Recently synthesis cassettes have become available, making (18)F-FDOPA more accessible. CONCLUSIONS: According to the available data, (18)F-FDOPA PET is a viable radiotracer for imaging and treatment planning of gliomas. ADVANCES IN KNOWLEDGE AND IMPLICATION FOR PATIENT CARE: (18)F-FDOPA PET appears to be a viable radiopharmaceutical for the diagnosis and treatment planning of gliomas cases, improving on that of MRI and (18)F-FDG PET.

Grey scale texture analysis of endobronchial ultrasound mini probe images for prediction of benign or malignant aetiology.

BACKGROUND AND OBJECTIVE: Expert analysis of endobronchial ultrasound mini probe (EBUS-MP) images has established subjective criteria for discriminating benign and malignant disease. Minimal data are available for objective analysis of these images. The aim of this study was to determine if greyscale texture analysis could differentiate between benign and malignant lung lesions. METHODS: Digital EBUS-MP images with a gain setting of 10/19 and contrast setting of 4/8 from 2007 until 2012 inclusive were included. These images had an expert-defined region of interest (ROI) mapped. ROI were analysed for the following greyscale texture features: mean pixel value, difference between maximum and minimum pixel value, standard deviation of the mean pixel value, entropy, correlation, energy and homogeneity. Significant greyscale texture features differentiating benign from malignant disease were used by two physicians to assess a validation set. RESULTS: A total of 167 images were available. The first 85 lesions were used in the prediction set. Benign lesions had larger differences between maximum and minimum pixel values, larger standard deviations of the mean pixel values and higher entropy than malignant lesions (P < 0.0001 for all values). A total of 82 peripheral lesions were in the validation set. Physician 1 correctly classified 63/82 (76.8%) with a negative predictive value (NPV) for malignancy of 82% and positive predictive value (PPV) of 75%. Physician 2 correctly classified 62/82 (75.6%) with a NPV of 100% and PPV of 71.0%. CONCLUSIONS: Greyscale texture analysis of EBUS-MP images can help establish aetiology with a high NPV for malignancy.

Robust inverse-consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy.

BACKGROUND: CT-MR registration is a critical component of many radiation oncology protocols. In prostate external beam radiation therapy, it allows the propagation of MR-derived contours to reference CT images at the planning stage, and it enables dose mapping during dosimetry studies. The use of carefully registered CT-MR atlases allows the estimation of patient specific electron density maps from MRI scans, enabling MRI-alone radiation therapy planning and treatment adaptation. In all cases, the precision and accuracy achieved by registration influences the quality of the entire process. PROBLEM: Most current registration algorithms do not robustly generalize and lack inverse-consistency, increasing the risk of human error and acting as a source of bias in studies where information is propagated in a particular direction, e.g. CT to MR or vice versa. In MRI-based treatment planning where both CT and MR scans serve as spatial references, inverse-consistency is critical, if under-acknowledged. PURPOSE: A robust, inverse-consistent, rigid/affine registration algorithm that is well suited to CT-MR alignment in prostate radiation therapy is presented. METHOD: The presented method is based on a robust block-matching optimization process that utilises a half-way space definition to maintain inverse-consistency. Inverse-consistency substantially reduces the influence of the order of input images, simplifying analysis, and increasing robustness. An open source implementation is available online at http://aehrc.github.io/Mirorr/. RESULTS: Experimental results on a challenging 35 CT-MR pelvis dataset demonstrate that the proposed method is more accurate than other popular registration packages and is at least as accurate as the state of the art, while being more robust and having an order of magnitude higher inverse-consistency than competing approaches. CONCLUSION: The presented results demonstrate that the proposed registration algorithm is readily applicable to prostate radiation therapy planning.

Hypoxia imaging in gliomas with 18F-fluoromisonidazole PET: toward clinical translation.

There is significant interest in the development of improved image-guided therapy for neuro-oncology applications. Glioblastomas (GBM) in particular present a considerable challenge because of their pervasive nature, propensity for recurrence, and resistance to conventional therapies. MRI is routinely used as a guide for planning treatment strategies. However, this imaging modality is not able to provide images that clearly delineate tumor boundaries and affords only indirect information about key tumor pathophysiology. With the emergence of PET imaging with new oncology radiotracers, mapping of tumor infiltration and other important molecular events such as hypoxia is now feasible within the clinical setting. In particular, the importance of imaging hypoxia levels within the tumoral microenvironment is gathering interest, as hypoxia is known to play a central role in glioma pathogenesis and resistance to treatment. One of the hypoxia radiotracers known for its clinical utility is (18)F-fluoromisodazole ((18)F-FMISO). In this review, we highlight the typical causes of treatment failure in gliomas that may be linked to hypoxia and outline current methods for the detection of hypoxia. We also provide an overview of the growing body of studies focusing on the clinical translation of (18)F-FMISO PET imaging, strengthening the argument for the use of (18)F-FMISO hypoxia imaging to help optimize and guide treatment strategies for patients with glioblastoma.

PET, MRI, and simultaneous PET/MRI in the development of diagnostic and therapeutic strategies for glioma.

Glioma is the most aggressive brain tumour, resulting in death often within 1-2 years. Current treatment strategies involve surgical resection followed by chemoradiation therapy. Despite continuing improvements in the delivery of adjuvant therapies, there has not been a dramatic increase in survival for glioma. Molecular imaging techniques have become central in the development of new therapeutic strategies in recent years. The multimodal imaging technology of positron emission tomography/magnetic resonance imaging (PET/MRI) has recently been realised on a preclinical scale and the effect of this technology is starting to be observed in preclinical drug development for glioma. Here, we propose that PET/MRI will play an integral part in the development of new diagnostic and therapeutic strategies for glioma.

Dual acquisition of (18)F-FMISO and (18)F-FDOPA.

Metabolic imaging using positron emission tomography (PET) has found increasing clinical use for the management of infiltrating tumours such as glioma. However, the heterogeneous biological nature of tumours and intrinsic treatment resistance in some regions means that knowledge of multiple biological factors is needed for effective treatment planning. For example, the use of (18)F-FDOPA to identify infiltrative tumour and (18)F-FMISO for localizing hypoxic regions. Performing multiple PET acquisitions is impractical in many clinical settings, but previous studies suggest multiplexed PET imaging could be viable. The fidelity of the two signals is affected by the injection interval, scan timing and injected dose. The contribution of this work is to propose a framework to explicitly trade-off signal fidelity with logistical constraints when designing the imaging protocol. The particular case of estimating (18)F-FMISO from a single frame prior to injection of (18)F-FDOPA is considered. Theoretical experiments using simulations for typical biological scenarios in humans demonstrate that results comparable to a pair of single-tracer acquisitions can be obtained provided protocol timings are carefully selected. These results were validated using a pre-clinical data set that was synthetically multiplexed. The results indicate that the dual acquisition of (18)F-FMISO and (18)F-FDOPA could be feasible in the clinical setting. The proposed framework could also be used to design protocols for other tracers.

Amorphous regions-of-interest projection method for simplified longitudinal comparison of dynamic regions in cancer imaging.

Tumors are typically analyzed as a single unit, despite their biologically heterogeneous nature. This limits correlations that can be drawn between regional variation and treatment outcome. Furthermore, despite the availability of high resolution 3-D medical imaging techniques, local outcomes, (e.g., tumor growth), are not easily measured. This paper proposes a method that uses streamlines to divide a 3-D region of interest (e.g., tumor) into units where local properties can be measured over the paths of growth. The parameters such as directional length and mean intensity can be measured locally at sequential time points and then compared. The method is evaluated on synthetic objects, simulated tumors, and medical images of brain tumors. The evaluations suggest that the method is suitable for mapping amorphous dynamic objects.