Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. However, it is hard for these methods to explicitly model the spatial relations between the labels in the output mask. Furthermore, the proportion of the background, OD and OC are unbalanced which also may result in a biased model as well as introduce more noise. To address these problems, we developed an approach that follows a coarse-to-fine segmentation process. We start with a U-Net to obtain a rough segmenting boundary and then crop the area around the boundary to form a boundary contour centered image. Second, inspired by sequence labeling tasks in natural language processing, we regard the OD and OC segmentation as a sequence labeling task and propose a novel fully convolutional network called SU-Net and combine it with the Viterbi algorithm to jointly decode the segmentation boundary. We also introduced a geometric parameter-based data augmentation method to generate more training samples in order to minimize the differences between training and test sets and reduce overfitting. Experimental results show that our method achieved state-of-the-art results on 2 datasets for both OD and OC segmentation and our method outperforms most of the ophthalmologists in terms of achieving agreement out of 6 ophthalmologists on the MESSIDOR dataset for both OD and OC segmentation. In terms of glaucoma screening, we achieved the best cup-to-disc ratio (CDR) error and area under the ROC curve (AUC) for glaucoma classification on the Drishti-GS dataset.

Development of an integrated biomechanics informatics system with knowledge discovery and decision support tools for research of injury prevention and performance enhancement.

The field of biomechanics involves integrating a variety of data types such as waveform, video, discrete, and performance. These different sources of data must be efficiently and accurately associated to provide meaningful feedback to athletes, coaches, and healthcare professionals to prevent injury and improve rehabilitation/performance. There are many challenges in biomechanics research such as data storage, standardization, review, sharing, and accessibility. Data is stored in different formats, structures, and locations such as physical hard drives or Dropbox/Google Drive, leading to issues during sharing and collaboration. Data is reviewed and analyzed through different software applications that need to be downloaded and installed locally before they are available for use. An integrated biomechanics informatics system (IBIS) built based on the core principles in medical imaging informatics provides a solution to many of these challenges. The system provides a secure web-based platform that will be accessible remotely for authenticated users to upload, share, and download data. The web-based application includes built-in data viewers that are streamlined for reviewing multimedia data and decision support/knowledge discovery tools. These tools include automatic foot contact detection for pre-processing, built-in statistical analysis applications for longitudinal and cross-study analysis, and a multi-institutional collaboration module. The IBIS system creates a centralized hub to support multi-institutional collaborative biomechanics research and analysis that is remotely accessible to all users including athletes, coaches, researchers, and clinicians generating a novel streamlined research workflow, data analysis, and knowledge discovery process.

Genetic Factors, Brain Atrophy, and Response to Rehabilitation Therapy After Stroke.

OBJECTIVE: Patients show substantial differences in response to rehabilitation therapy after stroke. We hypothesized that specific genetic profiles might explain some of this variance and, secondarily, that genetic factors are related to cerebral atrophy post-stroke. METHODS: The phase 3 ICARE study examined response to motor rehabilitation therapies. In 216 ICARE enrollees, DNA was analyzed for presence of the BDNF val66met and the ApoE ε4 polymorphism. The relationship of polymorphism status to 12-month change in motor status (Wolf Motor Function Test, WMFT) was examined. Neuroimaging data were also evaluated (n=127). RESULTS: Subjects were 61±13 years old (mean±SD) and enrolled 43±22 days post-stroke; 19.7% were BDNF val66met carriers and 29.8% ApoE ε4 carriers. Carrier status for each polymorphism was not associated with WMFT, either at baseline or over 12 months of follow-up. Neuroimaging, acquired 5±11 days post-stroke, showed that BDNF val66met polymorphism carriers had a 1.34-greater degree of cerebral atrophy compared to non-carriers (P=.01). Post hoc analysis found that age of stroke onset was 4.6 years younger in subjects with the ApoE ε4 polymorphism (P=.02). CONCLUSION: Neither the val66met BDNF nor ApoE ε4 polymorphism explained inter-subject differences in response to rehabilitation therapy. The BDNF val66met polymorphism was associated with cerebral atrophy at baseline, echoing findings in healthy subjects, and suggesting an endophenotype. The ApoE ε4 polymorphism was associated with younger age at stroke onset, echoing findings in Alzheimer's disease and suggesting a common biology. Genetic associations provide insights useful to understanding the biology of outcomes after stroke.

Evolving minimally invasive spine surgery: a surgeon's perspective on technological convergence and digital or control system.

Degenerated spinal disc and spinal stenosis are common problems requiring decompressive spinal surgery. Traditional open spinal discectomy is associated with significant tissue trauma, greater morbidity/complications, scarring, often longer term of convalescence, and even destabilization of the spine. Therefore, the pursuit of less traumatic minimally invasive spine surgery (MISS) began. The trend of spinal surgery is rapidly moving toward MISS. MISS is a technologically dependent surgery, and requires increased utilization of advanced endoscopic surgical instruments, imaging-video technology, and tissue modulation technology for performing spinal surgery in a digital operating room (DOR). It requires seamless connectivity and control to perform the surgical procedures in a precise and orchestrated manner. A new integrated DOR, the technological convergence and control system SurgMatix(R), was created in response to the need and to facilitate MISS with "organized control instead of organized chaos" in the endoscopic OR suite. It facilitates the performance, training, and further development of MISS.

Racial differences in growth patterns of children assessed on the basis of bone age.

PURPOSE: To collect up-to-date data in healthy children to create a digital hand atlas (DHA) that can be used to evaluate, on the basis of the Greulich and Pyle atlas method, racial differences in skeletal growth patterns of Asian, African American, white, and Hispanic children in the United States. MATERIALS AND METHODS: This retrospective study was HIPAA compliant and approved by the institutional review board. Informed consent was obtained from all subjects or their guardians. From May 1997 to March 2008, a DHA containing 1390 hand and wrist radiographs obtained in male and female Asian, African American, white, and Hispanic children with normal skeletal development was developed. The age of subjects ranged from 1 day to 18 years. Each image was read by two pediatric radiologists working independently and without knowledge of the subject's chronologic age, and evaluation was based on their experience with the Greulich and Pyle atlas. Statistical analyses were performed with the paired-samples t test and analysis of variance to study racial differences in growth patterns. P

Informatics in radiology: DICOM-RT and its utilization in radiation therapy.

The Digital Imaging and Communications in Medicine (DICOM) standard is now widely implemented in radiology as the standard for diagnostic imaging. It has also been extended for use in various sub-specialties. One of the first extensions was applied to radiation therapy and is known as DICOM-RT. In addition to the protocol used in the DICOM standard, seven DICOM-RT objects--namely, RT Image, RT Structure Set, RT Plan, RT Dose, RT Beams Treatment Record, RT Brachy Treatment Record, and RT Treatment Summary Record--have been created, each with a well-defined data model. The data models set the standard for integration of radiation therapy information for an electronic patient record and would facilitate the interoperability of different radiation therapy systems, thus making possible the sharing of information from different systems.

Informatics in radiology: DICOM-RT-based electronic patient record information system for radiation therapy.

Comprehensive clinical imaging data and additional relevant information are crucial for the planning and delivery of radiation therapy in patients with cancer. Multiple stand-alone systems that make use of technologic advances in imaging, treatment planning, and treatment delivery acquire or generate key data during the course of radiation therapy. However, the data are scattered in various systems throughout the radiation therapy department, thereby compromising efficient clinical work flow. In 1997 and 1999, the Digital Imaging and Communications in Medicine (DICOM) standard was extended from radiology to radiation therapy with the ratification of seven DICOM-RT objects. These objects helped set the standard for (a) data integration and interoperability between radiation therapy equipment and information systems from different manufacturers, and (b) the use of DICOM diagnostic images in radiation therapy. More recently, key radiation therapy imaging and informatics data have been integrated to form an open-architecture comprehensive radiation therapy electronic patient record (ePR) system. The benefits of such a DICOM-RT-based ePR system are threefold: it can be used as a foundation for performing effective and efficient clinical services, as a common platform for radiation therapy data exchange and expert consultation, and for medical imaging informatics research in developing innovative decision support tools and a knowledge base for improved treatment with radiation therapy.

An ultrasonic navigation system for endovascular aortic repair.

The objective of our research is to develop an ultrasound, time-of-flight triangulation based system for real-time, intraoperative 3-dimensional tracking of minimally invasive surgical instruments. We have chosen to first apply this technology towards tracking catheters used in endovascular aortic stent-grafting. To demonstrate the feasibility of this concept, we have developed a system involving a custom catheter based transducer and performed an experiment in excised porcine tissue with a model aorta.

A knowledge-based imaging informatics approach for managing proton beam therapy of cancer patients.

The need for a unified patient-oriented information system to handle complex proton therapy (PT) imaging and informatics data during the course of patient treatment is becoming steadily apparent due to the ever increasing demands for better diagnostic treatment planning and more accurate information. Currently, this information is scattered throughout each of the different treatment and information systems in the oncology department. Furthermore, the lack of organization with standardized methods makes it difficult and time-consuming to navigate through the maze of data, resulting in challenges during patient treatment planning. We present a methodology to develop this electronic patient record (ePR) system based on DICOM standards and perform knowledge-based medical imaging informatics research on specific clinical scenarios where patients are treated with PT. Treatment planning is similar in workflow to traditional radiation therapy (RT) methods such as intensity-modulated radiation therapy (IMRT), which utilizes a priori knowledge to drive the treatment plan in an inverse manner. In March 2006, two new RT objects were drafted in a DICOM-RT Supplement 102 specifically for ion therapy, which includes PT. The standardization of DICOM-RT-ION objects and the development of a knowledge base as well as decision-support tools that can be add-on features to the ePR DICOM-RT system were researched. This methodology can be used to extend to PT and the development of future clinical decision-making scenarios during the course of the patient's treatment that utilize "inverse treatment planning." We present the initial steps of this imaging and informatics methodology for PT and lay the foundation for development of future decision-support tools tailored to cancer patients treated with PT. By integrating decision-support knowledge and tools designed to assist in the decision-making process, a new and improved "knowledge-enhanced treatment planning" approach can be realized.

Experiences with a prototype tracking and verification system implemented within an imaging center.

RATIONALE AND OBJECTIVES: Most health care facilities currently struggle with protecting medical data privacy, misidentification of patients, and long patient waiting times. This article demonstrates a novel system for a clinical environment using wireless tracking and facial biometric technologies to automatically monitor and identify staff and patients to address these problems. MATERIALS AND METHODS: The design of the location tracking and verification system (LTVS) was based on a workflow study which was performed to observe the physical location and movement of patient and staff at the Healthcare Consultation Center II (HCC II) running hospital information systems, radiology information systems, picture archive and communication systems, and a voice recognition system. Based on the results from this workflow study, the LTVS was designed using a wireless real-time location system and a facial biometric system integrated with the radiology information system. The LTVS was tested for its functionality in a laboratory environment, then evaluated at HCC II. RESULTS: Experimental results in the laboratory and clinical environments demonstrated that patient and staff real-time location information and identity verification can be obtained from LTVS. Warning messages can immediately be sent to alert staff when patient's waiting time is over a predefined limit, and unauthorized access to a security area can be audited. Additionally, patient misidentification can be prevented during the course of examinations. CONCLUSIONS: The system enabled health care providers to streamline the patient workflow, protect against erroneous examinations and create a security zone to prevent, and audit unauthorized access to patient health care data required by the Health Insurance Portability and Accountability Act mandate.

Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones.

A computer-aided-diagnosis (CAD) method has been previously developed based on features extracted from phalangeal regions of interest (ROI) in a digital hand atlas, which can assess bone age of children from ages 7 to 18 accurately. Therefore, in order to assess the bone age of children in younger ages, the inclusion of carpal bones is necessary. However, due to various factors including the uncertain number of bones appearing, non-uniformity of soft tissue, low contrast between the bony structure and soft tissue, automatic segmentation and identification of carpal bone boundaries is an extremely challenging task. Past research works on carpal bone segmentation were performed utilizing dynamic thresholding. However, due to the limitation of the segmentation algorithm, carpal bones have not been taken into consideration in the bone age assessment procedure. In this paper, we developed and implemented a knowledge-based method for fully automatic carpal bone segmentation and morphological feature analysis. Fuzzy classification was then used to assess the bone age based on the selected features. This method has been successfully applied on all cases in which carpal bones have not overlapped. CAD results of total about 205 cases from the digital hand atlas were evaluated against subject chronological age as well as readings of two radiologists. It was found that the carpal ROI provides reliable information in determining the bone age for young children from newborn to 7-year-old.

CAD-PACS integration tool kit based on DICOM secondary capture, structured report and IHE workflow profiles.

Computer aided diagnosis/detection (CAD) goes beyond subjective visual assessment of clinical images providing quantitative computer analysis of the image content, and can greatly improve clinical diagnostic outcome. Many CAD applications, including commercial and research CAD, have been developed with no ability to integrate the CAD results with a clinical picture archiving and communication system (PACS). This has hindered the extensive use of CAD for maximum benefit within a clinical environment. In this paper, we present a CAD-PACS integration toolkit that integrates CAD results with a clinical PACS. The toolkit is a software package with two versions: DICOM (digital imaging and communications in medicine)-SC (secondary capture) and DICOM-IHE (Integrating the Healthcare Enterprise). The former uses the DICOM secondary capture object model to convert the screen shot of the CAD results to a DICOM image file for PACS workstations to display, while the latter converts the CAD results to a DICOM structured report (SR) based on IHE Workflow Profiles. The DICOM-SC method is simple and easy to be implemented without ability for further data mining of CAD results, while the DICOM-IHE can be used for data mining of CAD results in the future but more complicated to implement than the DICOM-SC method.

Image-assisted knowledge discovery and decision support in radiation therapy planning.

The need for quantified knowledge and decision-support tools to handle complex radiation therapy (RT) imaging and informatics data is becoming steadily apparent. Lessons can be learned from current CAD applications in radiology. This paper proposes a methodology to develop this quantified knowledge and decision-support tools to facilitate RT treatment planning. The methodology is applied to cancer patient cases treated by intensity modulated radiation therapy (IMRT). The use of the "inverse treatment planning" and imaging intensive nature of IMRT allows for the development of such image-assisted tools for supporting decision-making thus providing better workflow efficiency and more precise dose predictions.

Stroke Lesions in a Large Upper Limb Rehabilitation Trial Cohort Rarely Match Lesions in Common Preclinical Models.

BACKGROUND: Stroke patients with mild-moderate upper extremity motor impairments and minimal sensory and cognitive deficits provide a useful model to study recovery and improve rehabilitation. Laboratory-based investigators use lesioning techniques for similar goals. OBJECTIVE: To determine whether stroke lesions in an upper extremity rehabilitation trial cohort match lesions from the preclinical stroke recovery models used to drive translational research. METHODS: Clinical neuroimages from 297 participants enrolled in the Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) study were reviewed. Images were characterized based on lesion type (ischemic or hemorrhagic), volume, vascular territory, depth (cortical gray matter, cortical white matter, subcortical), old strokes, and leukoaraiosis. Lesions were compared with those of preclinical stroke models commonly used to study upper limb recovery. RESULTS: Among the ischemic stroke participants, median infarct volume was 1.8 mL, with most lesions confined to subcortical structures (61%) including the anterior choroidal artery territory (30%) and the pons (23%). Of ICARE participants, <1% had lesions resembling proximal middle cerebral artery or surface vessel occlusion models. Preclinical models of subcortical white matter injury best resembled the ICARE population (33%). Intracranial hemorrhage participants had small (median 12.5 mL) lesions that best matched the capsular hematoma preclinical model. CONCLUSIONS: ICARE subjects are not representative of all stroke patients, but they represent a clinically and scientifically important subgroup. Compared with lesions in general stroke populations and widely studied animal models of recovery, ICARE participants had smaller, more subcortically based strokes. Improved preclinical-clinical translational efforts may require better alignment of lesions between preclinical and human stroke recovery models.

Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer.

PURPOSE: Clinical data that are generated through routine radiation therapy procedures can be leveraged as a source of knowledge to provide evidence-based decision support for future patients. Treatment planning in radiation therapy often relies on trial-and-error iterations, experience, judgment calls and general guidelines. The authors present a knowledge-driven decision support system that assists clinicians by reducing some of the uncertainties associated with treatment planning and provides quantified empirical estimates to help minimize the radiation dose to healthy critical structures surrounding the tumor. METHODS: A database of retrospective DICOM RT data fuels a decision support engine, which assists clinicians in selecting dose constraints and assessing dose distributions. The first step is to quantify the spatial relationships between the tumor and surrounding critical structures through features that account for distance, volume, overlap, location, shape and orientation. These features are used to identify database cases that are anatomically similar to the new patient. The dose profiles of these database cases can help clinicians to estimate an acceptable dose distribution for the new case, based on empirical evidence. Since database diversity is essential for good system performance, an infrastructure for multi-institutional collaboration was also conceptualized in order to pave the way for data sharing of protected health information. RESULTS: A set of 127 retrospective test cases was collected from a single institution in order to conduct a leave-one-out evaluation of the decision support module. In 72 % of these retrospective test cases, patients with similar tumor anatomy were also found to exhibit similar radiation dose distributions. This demonstrates the system's ability to successfully extract retrospective database cases that can estimate the new patient's dose distribution. CONCLUSION: The radiation therapy treatment planning decision support system presented here can assist clinicians in determining good dose constraints and assessing dose distributions by using knowledge gained from retrospective treatment plans.

Development of a novel imaging informatics-based system with an intelligent workflow engine (IWEIS) to support imaging-based clinical trials.

Imaging based clinical trials can benefit from a solution to efficiently collect, analyze, and distribute multimedia data at various stages within the workflow. Currently, the data management needs of these trials are typically addressed with custom-built systems. However, software development of the custom-built systems for versatile workflows can be resource-consuming. To address these challenges, we present a system with a workflow engine for imaging based clinical trials. The system enables a project coordinator to build a data collection and management system specifically related to study protocol workflow without programming. Web Access to DICOM Objects (WADO) module with novel features is integrated to further facilitate imaging related study. The system was initially evaluated by an imaging based rehabilitation clinical trial. The evaluation shows that the cost of the development of system can be much reduced compared to the custom-built system. By providing a solution to customize a system and automate the workflow, the system will save on development time and reduce errors especially for imaging clinical trials.

HIPAA compliant auditing system for medical images.

As an official regulation for healthcare privacy and security, Health Insurance Portability and Accountability Act (HIPAA) mandates health institutions to protect health information against unauthorized use or disclosure. One such method proposed by HIPAA Security Standards is audit trail, which records and examines health information access activities. HIPAA mandates healthcare providers to have the ability to generate audit trails on data access activities for any specific patient. Although current medical imaging systems generate activity logs, there is a lack of formal methodology to interpret these large volumes of log data and generate HIPAA compliant auditing trails. This paper outlines the design of a HIPAA compliant auditing system (HCAS) for medical images in imaging systems such as PACS and discusses the development of a security monitoring (SM) toolkit based on some of the partial components in HCAS.

Utilizing data grid architecture for the backup and recovery of clinical image data.

Grid Computing represents the latest and most exciting technology to evolve from the familiar realm of parallel, peer-to-peer and client-server models. However, there has been limited investigation into the impact of this emerging technology in medical imaging and informatics. In particular, PACS technology, an established clinical image repository system, while having matured significantly during the past ten years, still remains weak in the area of clinical image data backup. Current solutions are expensive or time consuming and the technology is far from foolproof. Many large-scale PACS archive systems still encounter downtime for hours or days, which has the critical effect of crippling daily clinical operations. In this paper, a review of current backup solutions will be presented along with a brief introduction to grid technology. Finally, research and development utilizing the grid architecture for the recovery of clinical image data, in particular, PACS image data, will be presented. The focus of this paper is centered on applying a grid computing architecture to a DICOM environment since DICOM has become the standard for clinical image data and PACS utilizes this standard. A federation of PACS can be created allowing a failed PACS archive to recover its image data from others in the federation in a seamless fashion. The design reflects the five-layer architecture of grid computing: Fabric, Resource, Connectivity, Collective, and Application Layers. The testbed Data Grid is composed of one research laboratory and two clinical sites. The Globus 3.0 Toolkit (Co-developed by the Argonne National Laboratory and Information Sciences Institute, USC) for developing the core and user level middleware is utilized to achieve grid connectivity. The successful implementation and evaluation of utilizing data grid architecture for clinical PACS data backup and recovery will provide an understanding of the methodology for using Data Grid in clinical image data backup for PACS, as well as establishment of benchmarks for performance from future grid technology improvements. In addition, the testbed can serve as a road map for expanded research into large enterprise and federation level data grids to guarantee CA (Continuous Availability, 99.999% up time) in a variety of medical data archiving, retrieval, and distribution scenarios.

Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients.

PURPOSE: MRI has been used to identify multiple sclerosis (MS) lesions in brain and spinal cord visually. Integrating patient information into an electronic patient record system has become key for modern patient care in medicine in recent years. Clinically, it is also necessary to track patients' progress in longitudinal studies, in order to provide comprehensive understanding of disease progression and response to treatment. As the amount of required data increases, there exists a need for an efficient systematic solution to store and analyze MS patient data, disease profiles, and disease tracking for both clinical and research purposes. METHOD: An imaging informatics based system, called MS eFolder, has been developed as an integrated patient record system for data storage and analysis of MS patients. The eFolder system, with a DICOM-based database, includes a module for lesion contouring by radiologists, a MS lesion quantification tool to quantify MS lesion volume in 3D, brain parenchyma fraction analysis, and provide quantitative analysis and tracking of volume changes in longitudinal studies. Patient data, including MR images, have been collected retrospectively at University of Southern California Medical Center (USC) and Los Angeles County Hospital (LAC). The MS eFolder utilizes web-based components, such as browser-based graphical user interface (GUI) and web-based database. The eFolder database stores patient clinical data (demographics, MS disease history, family history, etc.), MR imaging-related data found in DICOM headers, and lesion quantification results. Lesion quantification results are derived from radiologists' contours on brain MRI studies and quantified into 3-dimensional volumes and locations. Quantified results of white matter lesions are integrated into a structured report based on DICOM-SR protocol and templates. The user interface displays patient clinical information, original MR images, and viewing structured reports of quantified results. The GUI also includes a data mining tool to handle unique search queries for MS. System workflow and dataflow steps has been designed based on the IHE post-processing workflow profile, including workflow process tracking, MS lesion contouring and quantification of MR images at a post-processing workstation, and storage of quantitative results as DICOM-SR in DICOM-based storage system. The web-based GUI is designed to display zero-footprint DICOM web-accessible data objects (WADO) and the SR objects. SUMMARY: The MS eFolder system has been designed and developed as an integrated data storage and mining solution in both clinical and research environments, while providing unique features, such as quantitative lesion analysis and disease tracking over a longitudinal study. A comprehensive image and clinical data integrated database provided by MS eFolder provides a platform for treatment assessment, outcomes analysis and decision-support. The proposed system serves as a platform for future quantitative analysis derived automatically from CAD algorithms that can also be integrated within the system for individual disease tracking and future MS-related research. Ultimately the eFolder provides a decision-support infrastructure that can eventually be used as add-on value to the overall electronic medical record.

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis.

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.