Automated temporalis muscle quantification and growth charts for children through adulthood

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Automated Sarcopenia Assessment and Outcomes in Head and Neck Cancer with Deep Learning Analysis of Cervical Neck Skeletal Muscle

PURPOSE/OBJECTIVE(S)

Sarcopenia is an established prognostic factor in patients diagnosed with head and neck cancers (HNC), typically measured by the skeletal muscle index (SMI) from abdominal muscle mass at L3. While sarcopenia assessment could inform HNC management, it remains impractical, time- and labor-intensive, and operator-dependent. To overcome these challenges, we developed an automated deep learning (DL) platform to calculate SMI at L3 by quantifying cross-sectional cervical skeletal muscle area (SMA) at C3 through auto-segmentation, externally validated it, and evaluated associations with clinical outcomes.

MATERIALS/METHODS

Eight hundred twenty-one patients diagnosed with HNC from multiple institutes from 1999-2013, treated with definitive chemoradiation with baseline pre-treatment CT scans, were included for model development (335 training, 96 tuning) and for independent testing (48 internal, and 342 external). Ground truth single-slice segmentations of SM at the mid-C3 vertebral level were manually annotated by radiation oncologists using an established protocol. A multi-stage DL pipeline was developed, with a 2D DenseNet to select the middle slice of C3 section and a 2D UNet to segment the SM, from which SMA was calculated. The model was evaluated using the Dice Similarity Coefficient (DC) for the internal test set, and human acceptability testing on the external test set was performed by two radiation oncologists not involved in annotations. SMI was calculated from C3 SMA based on prior literature, and sarcopenia was defined by an established, sex-specific SMI cutoff. Sarcopenia associations with overall survival (OS) and toxicities were assessed on the external dataset with Cox and logistic multivariable regressions, as indicated.

RESULTS

Model DC on the internal test set as 0.90 [95% CI: 0.90-0.91], with an intra-class coefficient of 0.96 for SMA. Human acceptability testing showed a pass rate of 94.4%. Of the 342 patients in the clinical analysis, 261 (76.3%) patients had sarcopenia. Five-year survival was 84.4% in patients without sarcopenia vs 73.1% in patients with sarcopenia (HR 2.21, p = 0.028) (median f/u: 44 mo (IQR: 25 - 66 mo)). On multivariable regression, sarcopenia (HR 2.06, p = 0.037), ACE-27 score 2+ (HR 2.25, p = 0.001), non-oropharynx diagnosis (HR 3.96, p<0.001), and T3-4 stage (HR 2.37, p<0.001) were associated with worse OS. Sarcopenia was associated with longer PEG tube duration on multivariable analysis (HR 1.59, p = 0.003), along with ACE-27 score (HR 1.20, p = 0.012) and non-oropharynx primary site (HR 1.46, p = 0.034). Sarcopenia was associated with higher risk of having PEG tube at last follow up (OR 2.25, p = 0.046). An observed increase in risk of hospitalization <3 months after RT was non-significant (OR 2.18, p = 0.117).

CONCLUSION

We developed and externally validated a fully-automated platform for sarcopenia assessment that can be used on routine HNC imaging. This algorithm is positioned for prospective testing to determine if use will inform HNC management.

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer

Importance

Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use.

Objective

To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes.

Design, Setting, and Participants

For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023.

Exposure

C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC.

Main Outcomes and Measures

Overall survival and treatment toxicity outcomes of HNSCC.

Results

The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia.

Conclusions and Relevance

This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial

BACKGROUND

Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making.

METHODS

For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic.

FINDINGS

From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger.

INTERPRETATION

The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool.

FUNDING

ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.

What is the prevalence of developmental prosopagnosia? An empirical assessment of different diagnostic cutoffs

The prevalence of developmental prosopagnosia (DP), lifelong face recognition deficits, is widely reported to be 2-2.5%. However, DP has been diagnosed in different ways across studies, resulting in differing prevalence rates. In the current investigation, we estimated the range of DP prevalence by administering well-validated objective and subjective face recognition measures to an unselected web-based sample of 3116 18-55 year-olds and applying DP diagnostic cutoffs from the last 14 years. We found estimated prevalence rates ranged from .64-5.42% when using a z-score approach and .13-2.95% when using a percentile approach, with the most commonly used cutoffs by researchers having a prevalence rate of .93% (z-score, .45% when using percentiles). We next used multiple cluster analyses to examine whether there was a natural grouping of poorer face recognizers but failed to find consistent grouping beyond those with generally above versus below average face recognition. Lastly, we investigated whether DP studies with more relaxed diagnostic cutoffs were associated with better performance on the Cambridge Face Perception Test. In a sample of 43 studies, there was a weak nonsignificant association between greater diagnostic strictness and better DP face perception accuracy (Kendall's tau-b correlation, τb =.18 z-score; τb = .11 percentiles). Together, these results suggest that researchers have used more conservative DP diagnostic cutoffs than the widely reported 2-2.5% prevalence. We discuss the strengths and weaknesses of using more inclusive cutoffs, such as identifying mild and major forms of DP based on DSM-5.

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline

Purpose

Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes.

Materials and Methods

899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression.

Results

DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r β 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis.

Conclusion

We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC.

SUMMARY STATEMENT

In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.

Intimate partner violence and brain imaging in women: A neuroimaging literature review

PRIMARY OBJECTIVE

Despite a high prevalence of intimate partner violence (IPV) and its lasting impacts on individuals, particularly women, very little is known about how IPV may impact the brain. IPV is known to frequently result in traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD). In this overview of literature, we examined literature related to neuroimaging in women with IPV experiences between the years 2010-2021.

RESEARCH DESIGN

Literature overview.

METHODS AND PROCEDURES

A total of 17 studies were included in the review, which is organized into each imaging modality, including magnetic resonance imaging (structural, diffusion, and functional MRI), Electroencephalography (EEG), proton magnetic resonance spectroscopy (pMRS), and multimodal imaging.

MAIN OUTCOMES AND RESULTS

Research has identified changes in brain regions associated with cognition, emotion, and memory. Howeverto date, it is difficult to disentangle the unique contributions of TBI and PTSD effects of IPV on the brain. Furthermore, experimental design elements differ considerably among studies.

CONCLUSIONS

The aim is to provide an overview of existing literature to determine commonalities across studies and to identify remaining knowledge gaps and recommendations for implementing future imaging studies with individuals who experience IPV.

Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout (n = 216) and external (n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout (n = 53) and external (n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022.

Egocentric video: a new tool for capturing hand use of individuals with spinal cord injury at home

Background

Current upper extremity outcome measures for persons with cervical spinal cord injury (cSCI) lack the ability to directly collect quantitative information in home and community environments. A wearable first-person (egocentric) camera system is presented that aims to monitor functional hand use outside of clinical settings.

Methods

The system is based on computer vision algorithms that detect the hand, segment the hand outline, distinguish the user’s left or right hand, and detect functional interactions of the hand with objects during activities of daily living. The algorithm was evaluated using egocentric video recordings from 9 participants with cSCI, obtained in a home simulation laboratory. The system produces a binary hand-object interaction decision for each video frame, based on features reflecting motion cues of the hand, hand shape and colour characteristics of the scene.

Results

The output from the algorithm was compared with a manual labelling of the video, yielding F1-scores of 0.74 ± 0.15 for the left hand and 0.73 ± 0.15 for the right hand. From the resulting frame-by-frame binary data, functional hand use measures were extracted: the amount of total interaction as a percentage of testing time, the average duration of interactions in seconds, and the number of interactions per hour. Moderate and significant correlations were found when comparing these output measures to the results of the manual labelling, with ρ = 0.40, 0.54 and 0.55 respectively.

Conclusions

These results demonstrate the potential of a wearable egocentric camera for capturing quantitative measures of hand use at home.

Validating Accelerometry as a Measure of Arm Movement for Children With Hemiplegic Cerebral Palsy

BACKGROUND

For children with hemiplegic cerebral palsy (HCP), rehabilitation aims to increase movement of the affected arm. However, no validated measure objectively examines this construct in pediatric practice or daily life.

OBJECTIVE

The objective of this study was to evaluate the criterion and known-groups validity of accelerometry as a measure of arm movement in children and adolescents with HCP.

DESIGN

This was a prospective cross-sectional study.

METHODS

Twenty-seven children and adolescents with typical development (3.4-13.9 years old) and 11 children and adolescents with HCP (4.7-14.7 years old; Manual Ability Classification System rating I or II) wore accelerometers on their wrists while engaged in 20 minutes of play, which included intermittent intervals of stillness and vigorous movement of the arms. Vector magnitude (VM) values identified the presence (VM > 2.0 counts per epoch) and absence (VM ≤ 2.0 counts per epoch) of arm movement for every 2-second epoch. Video was simultaneously recorded; each 2-second interval of footage was scored as "movement" or "no movement" for each arm.

RESULTS

Agreement between accelerometry and video observation was greater than or equal to 81%, and the prevalence-adjusted and bias-adjusted κ value was greater than or equal to 0.69 for both groups of participants; these results supported the criterion validity of accelerometry. The ratio of nondominant arm movement to dominant arm movement measured by accelerometry was significantly greater in participants with typical development (mean [SD] = 0.87 [0.09]) than in participants with HCP (mean = 0.78 [0.07]) on the basis of 10 age- and sex-matched pairs; these results supported known-groups validity.

LIMITATIONS

The small sample size of the group with HCP prevented the stratification of data by age. Participants with HCP had high or moderately high function of the affected arm; hence, the findings do not apply to children and adolescents with more significant hemiparesis.

CONCLUSIONS

Accelerometry is a valid measure of arm movement in children with HCP and children without HCP. These findings contribute to the development of innovative upper limb assessments for children with hemiparesis.

EEG-Controlled Functional Electrical Stimulation Therapy With Automated Grasp Selection: A Proof-of-Concept Study

Background: Functional electrical stimulation therapy (FEST) is a promising intervention for the restoration of upper extremity function after cervical spinal cord injury (SCI). Objectives: This study describes and evaluates a novel FEST system designed to incorporate voluntary movement attempts and massed practice of functional grasp through the use of brain-computer interface (BCI) and computer vision (CV) modules. Methods: An EEG-based BCI relying on a single electrode was used to detect movement initiation attempts. A CV system identified the target object and selected the appropriate grasp type. The required grasp type and trigger command were sent to an FES stimulator, which produced one of four multichannel muscle stimulation patterns (precision, lateral, palmar, or lumbrical grasp). The system was evaluated with five neurologically intact participants and one participant with complete cervical SCI. Results: An integrated BCI-CV-FES system was demonstrated. The overall classification accuracy of the CV module was 90.8%, when selecting out of a set of eight objects. The average latency for the BCI module to trigger the movement across all participants was 5.9 ± 1.5 seconds. For the participant with SCI alone, the CV accuracy was 87.5% and the BCI latency was 5.3 ± 9.4 seconds. Conclusion: BCI and CV methods can be integrated into an FEST system without the need for costly resources or lengthy setup times. The result is a clinically relevant system designed to promote voluntary movement attempts and more repetitions of varied functional grasps during FEST.

Influence of upper limb movement patterns on accelerometer measurements: a pediatric case series

OBJECTIVE

Previous studies showed success using wrist-worn accelerometers to monitor upper-limb activity in adults and children with hemiparesis. However, a knowledge gap exists regarding which specific joint movements are reflected in accelerometry readings. We conducted a case series intended to enrich data interpretation by characterizing the influence of different pediatric upper-limb movements on accelerometry data.

APPROACH

The study recruited six typically developing children and five children with hemiparetic cerebral palsy. The participants performed unilateral and bilateral activities, and their upper limb movements were measured with wrist-worn accelerometers and the Microsoft Kinect, a markerless motion-capture system that tracks skeletal data. The Kinect data were used to quantify specific upper limb movements through joint angle calculations (trunk, shoulder, elbow and wrist). Correlation coefficients (r) were calculated to quantify the influence of individual joint movements on accelerometry data. Regression analyses were performed to examine multi-joint patterns and explain variability across different activities and participants.

MAIN RESULTS

Single-joint correlation results suggest that pediatric wrist-worn accelerometry data are not biased to particular individual joint movements. Rather, the accelerometry data could best be explained by the movements of the joints with the most functional relevance to the performed activity.

SIGNIFICANCE

This case series provides deeper insight into the interpretation of wrist-worn accelerometry data, and supports the use of this tool in quantifying functional upper-limb movements in pediatric populations.

Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community

OBJECTIVE

Hand function impairment after cervical spinal cord injury (SCI) can significantly reduce independence. Unlike current hand function assessments, wearable camera systems could potentially measure functional hand usage at home, and thus benefit the development of neurorehabilitation strategies. The objective of this study was to understand the views of individuals with SCI on the use of wearable cameras to track neurorehabilitation progress and outcomes in the community.

DESIGN

Questionnaires.

SETTING

Home simulation laboratory.

PARTICIPANTS

Fifteen individuals with cervical SCI.

OUTCOME MEASURES

After using wearable cameras in the simulated home environment, participants completed custom questionnaires, comprising open-ended and structured questions.

RESULTS

Participants showed relatively low concerns related to data confidentiality when first-person videos are used by clinicians (1.93 ± 1.28 on a 5-point Likert scale) or researchers (2.00 ± 1.31). Storing only automatically extracted metrics reduced privacy concerns. Though participants reported moderate privacy concerns (2.53 ± 1.51) about wearing a camera in daily life due to certain sensitive situations (e.g. washrooms), they felt that information about their hand usage at home is useful for researchers (4.73 ± 0.59), clinicians (4.47 ± 0.83), and themselves (4.40 ± 0.83). Participants found the system moderately comfortable (3.27 ± 1.44), but expressed low desire to use it frequently (2.87 ± 1.36).

CONCLUSION

Despite some privacy and comfort concerns, participants believed that the information obtained would be useful. With appropriate strategies to minimize the data stored and recording duration, wearable cameras can be a well-accepted tool to track function in the home and community after SCI.

C. elegans tracking and behavioral measurement

We have developed instrumentation, image processing, and data analysis techniques to quantify the locomotory behavior of C. elegans as it crawls on the surface of an agar plate. For the study of the genetic, biochemical, and neuronal basis of behavior, C. elegans is an ideal organism because it is genetically tractable, amenable to microscopy, and shows a number of complex behaviors, including taxis, learning, and social interaction. Behavioral analysis based on tracking the movements of worms as they crawl on agar plates have been particularly useful in the study of sensory behavior, locomotion, and general mutational phenotyping. Our system works by moving the camera and illumination system as the worms crawls on a stationary agar plate, which ensures no mechanical stimulus is transmitted to the worm. Our tracking system is easy to use and includes a semi-automatic calibration feature. A challenge of all video tracking systems is that it generates an enormous amount of data that is intrinsically high dimensional. Our image processing and data analysis programs deal with this challenge by reducing the worms shape into a set of independent components, which comprehensively reconstruct the worms behavior as a function of only 3-4 dimensions. As an example of the process we show that the worm enters and exits its reversal state in a phase specific manner.