Semi-automatic Image Segmentation Research Articles

Three dimensional imaging of paraffin embedded human lung tissue samples by micro-computed tomography.

BackgroundUnderstanding the three-dimensional (3-D) micro-architecture of lung tissue can provide insights into the pathology of lung disease. Micro computed tomography (µCT) has previously been used to elucidate lung 3D histology and morphometry in fixed samples that have been stained with contrast agents or air inflated and dried. However, non-destructive microstructural 3D imaging of formalin-fixed paraffin embedded (FFPE) tissues would facilitate retrospective analysis of extensive tissue archives of lung FFPE lung samples with linked clinical data.MethodsFFPE human lung tissue samples (n = 4) were scanned using a Nikon metrology µCT scanner. Semi-automatic techniques were used to segment the 3D structure of airways and blood vessels. Airspace size (mean linear intercept, Lm) was measured on µCT images and on matched histological sections from the same FFPE samples imaged by light microscopy to validate µCT imaging.ResultsThe µCT imaging protocol provided contrast between tissue and paraffin in FFPE samples (15mm x 7mm). Resolution (voxel size 6.7 µm) in the reconstructed images was sufficient for semi-automatic image segmentation of airways and blood vessels as well as quantitative airspace analysis. The scans were also used to scout for regions of interest, enabling time-efficient preparation of conventional histological sections. The Lm measurements from µCT images were not significantly different to those from matched histological sections.ConclusionWe demonstrated how non-destructive imaging of routinely prepared FFPE samples by laboratory µCT can be used to visualize and assess the 3D morphology of the lung including by morphometric analysis.

Semi-Automated Building Footprint Extraction From Orthophotos

Here we describe and apply a semi-automated, object-based method for extracting vector-building footprint polygons from aerial photographs (orthophotos) within urban settings. The approach integrates the use of high resolution orthophotos and image segmentation software and is compared with methods using Light Detection and Ranging (LiDAR) as the source data input. LiDAR data gives the best results with less processing, but is not widely used by municipalities due to the expense. Results from semi-automated image segmentation of the orthophotos showed a high accuracy between extracted building segments and reference building footprints for two study sites, comparable to those achieved using LiDAR data. We recommend image acquisition during summer months with a resolution of 10 cm by 10 cm. When data acquisition budgets are limited, combining ancillary GIS on roads with a semi-automated and object-based segmentation approach is a best practice strategy for land cover feature extraction and change quantification.

Accuracy of 3D dual echo steady state (DESS) MR arthrography to quantify acetabular cartilage thickness.

To deploy and quantify the accuracy of 3D dual echo steady state (DESS) MR arthrography with hip traction to image acetabular cartilage. Clinical magnetic resonance imaging (MRI) sequences used to image hip cartilage often have reduced out-of-plane resolution and may lack adequate signal-to-noise to image cartilage. Saline was injected into four cadaver hips placed under traction. 3D DESS MRI scans were obtained before and after cores of cartilage were harvested from the acetabulum; the two MRIs were spatially aligned to reference core positions. The thickness of cartilage cores was measured under microscopy to serve as the reference standard. 3D reconstructions of cartilage and subchondral bone were generated using automatic and semiautomatic image segmentation. Cartilage thickness estimated from the 3D reconstructions was compared to physical measurements using Bland-Altman plots. As revealed by the automatic segmentation mask, saline imbibed the joint space throughout the articulating surface, with the exception of the posteroinferior region in two hips. Locations where air bubbles were introduced and regions of suspected low density bone disrupted an otherwise smooth automatic segmentation mask. Automatic and semiautomatic segmentation yielded a bias ± repeatability coefficient (95% limits of agreement) of 0.10 ± 0.51 mm (-0.41 to 0.61 mm) and 0.06 ± 0.43 mm (-0.37 to 0.49 mm), respectively. Cartilage thickness can be estimated to within ∼0.5 mm of the physical value with 95% confidence using 3D reconstructions of 3D DESS MR arthrography images. Manual correction of the automatic segmentation mask may improve reconstruction accuracy.

Semiautomatic segmentation of longitudinal computed tomography images in a rat model of lung injury by surfactant depletion.

Quantitative analysis of computed tomography (CT) is essential to the study of acute lung injury. However, quantitative CT is made difficult by poor lung aeration, which complicates the critical step of image segmentation. To overcome this obstacle, this study sought to develop and validate a semiautomated, multilandmark, registration-based scheme for lung segmentation that is effective in conditions of poor aeration. Expiratory and inspiratory CT images were obtained in rats (n = 8) with surfactant depletion of incremental severity to mimic worsening aeration. Trained operators manually delineated the images to provide a comparative landmark. Semiautomatic segmentation originated from a single, previously segmented reference image obtained at healthy baseline. Deformable registration of the target images (after surfactant depletion) was performed using the symmetric diffeomorphic transformation model with B-spline regularization. Registration used multiple landmarks (i.e., rib cage, spine, and lung parenchyma) to minimize the effect of poor aeration. Then target images were automatically segmented by applying the calculated transformation function to the reference image contour. Semiautomatically and manually segmented contours proved to be highly similar in all aeration conditions, including those characterized by more severe surfactant depletion and expiration. The Dice similarity coefficient was over 0.9 in most conditions, confirming high agreement, irrespective of poor aeration. Furthermore, CT density-based measurements of gas volume, tissue mass, and lung aeration distribution were minimally affected by the method of segmentation. Moving forward, multilandmark registration has the potential to streamline quantitative CT analysis by enabling semiautomatic image segmentation of lungs with a broad range of injury severity.

Spinal cord damage in Machado-Joseph disease.

Machado-Joseph disease (SCA3) is the most frequent spinocerebellar ataxia worldwide and characterized by remarkable phenotypic heterogeneity. MRI-based studies in SCA3 focused in the cerebellum and connections, but little is known about cord damage in the disease and its clinical relevance. To evaluate the spinal cord damage in SCA3 through quantitative analysis of MRI scans. A group of 48 patients with SCA3 and 48 age and gender-matched healthy controls underwent MRI on a 3T scanner. We used T1-weighted 3D images to estimate the cervical spinal cord area (CA) and eccentricity (CE) at three C2/C3 levels based on a semi-automatic image segmentation protocol. The scale for assessment and rating of ataxia (SARA) was employed to quantify disease severity. The two groups-SCA3 and controls-were significantly different regarding CA (49.5 ± 7.3 vs 67.2 ± 6.3mm(2), p < 0.001) and CE values (0.79 ± 0.06 vs 0.75 ± 0.05, p = 0.005). In addition, CA presented a significant correlation with SARA scores in the patient group (p = 0.010). CE was not associated with SARA scores (p = 0.857). In the multiple variable regression, we found that disease duration was the only variable associated with CA (coefficient = -0.629, p = 0.025). SCA3 is characterized by cervical cord atrophy and antero-posterior flattening. In addition, the spinal cord areas did correlate with disease severity. This suggests that quantitative analyses of the spinal cord MRI might be a useful biomarker in SCA3.

Image Segmentation and Analysis of Flexion-Extension Radiographs of Cervical Spines.

We present a new analysis tool for cervical flexion-extension radiographs based on machine vision and computerized image processing. The method is based on semiautomatic image segmentation leading to detection of common landmarks such as the spinolaminar (SL) line or contour lines of the implanted anterior cervical plates. The technique allows for visualization of the local curvature of these landmarks during flexion-extension experiments. In addition to changes in the curvature of the SL line, it has been found that the cervical plates also deform during flexion-extension examination. While extension radiographs reveal larger curvature changes in the SL line, flexion radiographs on the other hand tend to generate larger curvature changes in the implanted cervical plates. Furthermore, while some lordosis is always present in the cervical plates by design, it actually decreases during extension and increases during flexion. Possible causes of this unexpected finding are also discussed. The described analysis may lead to a more precise interpretation of flexion-extension radiographs, allowing diagnosis of spinal instability and/or pseudoarthrosis in already seemingly fused spines.

A semi-automated image segmentation approach for computational fluid dynamics studies of aortic dissection.

Computational studies of aortic hemodynamics require accurate and reproducible segmentation of the aortic tree from whole body, contrast enhanced CT images. Three methods were vetted for segmentation. A semi-automated approach that utilizes denoising, the extended maxima transform, and a minimal amount of manual segmentation was adopted.

Three-Dimensional Ultrasound-Derived Physical Mitral Valve Modeling

Advances in mitral valve repair and adoption have been partly attributed to improvements in echocardiographic imaging technology. To educate and guide repair surgery further, we have developed a methodology for fast production of physical models of the valve using novel three-dimensional (3D) echocardiographic imaging software in combination with stereolithographic printing. Quantitative virtual mitral valve shape models were developed from 3D transesophageal echocardiographic images using software based on semiautomated image segmentation and continuous medial representation algorithms. These quantitative virtual shape models were then used as input to a commercially available stereolithographic printer to generate a physical model of the each valve at end systole and end diastole. Physical models of normal and diseased valves (ischemic mitral regurgitation and myxomatous degeneration) were constructed. There was good correspondence between the virtual shape models and physical models. It was feasible to create a physical model of mitral valve geometry under normal, ischemic, and myxomatous valve conditions using 3D printing of 3D echocardiographic data. Printed valves have the potential to guide surgical therapy for mitral valve disease.

TU‐A‐9A‐06: Semi‐Automatic Segmentation of Skin Cancer in High‐Frequency Ultrasound Images: Initial Comparison with Histology

Purpose:In skin‐cancer radiotherapy, the assessment of skin lesion is challenging, particularly with important features such as the depth and width hard to determine. The aim of this study is to develop interative segmentation method to delineate tumor boundary using high‐frequency ultrasound images and to correlate the segmentation results with the histopathological tumor dimensions.Methods:We analyzed 6 patients who comprised a total of 10 skin lesions involving the face, scalp, and hand. The patient’s various skin lesions were scanned using a high‐frequency ultrasound system (Episcan, LONGPORT, INC., PA, U.S.A), with a 30‐MHz single‐element transducer. The lateral resolution was 14.6 micron and the axial resolution was 3.85 micron for the ultrasound image. Semiautomatic image segmentation was performed to extract the cancer region, using a robust statistics driven active contour algorithm. The corresponding histology images were also obtained after tumor resection and served as the reference standards in this study.Results:Eight out of the 10 lesions are successfully segmented. The ultrasound tumor delineation correlates well with the histology assessment, in all the measurements such as depth, size, and shape. The depths measured by the ultrasound have an average of 9.3% difference comparing with that in the histology images. The remaining 2 cases suffered from the situation of mismatching between pathology and ultrasound images.Conclusion:High‐frequency ultrasound is a noninvasive, accurate and easy‐accessible modality to image skin cancer. Our segmentation method, combined with high‐frequency ultrasound technology, provides a promising tool to estimate the extent of the tumor to guide the radiotherapy procedure and monitor treatment response.

An intra-individual comparison of MRI, [18F]-FET and [18F]-FLT PET in patients with high-grade gliomas.

ObjectivesIntra-individual spatial overlap analysis of tumor volumes assessed by MRI, the amino acid PET tracer [18F]-FET and the nucleoside PET tracer [18F]-FLT in high-grade gliomas (HGG).MethodsMRI, [18F]-FET and [18F]-FLT PET data sets were retrospectively analyzed in 23 HGG patients. Morphologic tumor volumes on MRI (post-contrast T1 (cT1) and T2 images) were calculated using a semi-automatic image segmentation method. Metabolic tumor volumes for [18F]-FET and [18F]-FLT PETs were determined by image segmentation using a threshold-based volume of interest analysis. After co-registration with MRI the morphologic and metabolic tumor volumes were compared on an intra-individual basis in order to estimate spatial overlaps using the Spearman's rank correlation coefficient and the Mann-Whitney U test.Results[18F]-FLT uptake was negative in tumors with no or only moderate contrast enhancement on MRI, detecting only 21 of 23 (91%) HGG. In addition, [18F]-FLT uptake was mainly restricted to cT1 tumor areas on MRI and [18F]-FLT volumes strongly correlated with cT1 volumes (r = 0.841, p<0.001). In contrast, [18F]-FET PET detected 22 of 23 (96%) HGG. [18F]-FET uptake beyond areas of cT1 was found in 61% of cases and [18F]-FET volumes showed only a moderate correlation with cT1 volumes (r = 0.573, p<0.001). Metabolic tumor volumes beyond cT1 tumor areas were significantly larger for [18F]-FET compared to [18F]-FLT tracer uptake (8.3 vs. 2.7 cm3, p<0.001).ConclusionIn HGG [18F]-FET but not [18F]-FLT PET was able to detect metabolic active tumor tissue beyond contrast enhancing tumor on MRI. In contrast to [18F]-FET, blood-brain barrier breakdown seems to be a prerequisite for [18F]-FLT tracer uptake.

Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts.

Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation.

Validation of semiautomated and locally resolved aortic wall thickness measurements from computed tomography

Aortic wall thickness (AWT) is important for anatomic description and biomechanical modeling of aneurysmal disease. However, no validated, noninvasive method for measuring AWT exists. We hypothesized that semiautomated image segmentation algorithms applied to computed tomography angiography (CTA) can accurately measure AWT. Aortic samples from 10 patients undergoing open thoracoabdominal aneurysm repair were taken from sites of the proximal or distal anastomosis, or both, yielding 13 samples. Aortic specimens were fixed in formalin, embedded in paraffin, and sectioned. After staining with hematoxylin and eosin and Masson's trichrome, sections were digitally scanned and measured. Patients' preoperative CTA Digital Imaging and Communications in Medicine (DICOM; National Electrical Manufacturers Association, Rosslyn, Va) images were segmented into luminal, inner arterial, and outer arterial surfaces with custom algorithms using active contours, isoline contour detection, and texture analysis. AWT values derived from image data were compared with measurements of corresponding pathologic specimens. AWT determined by CTA averaged 2.33 ± 0.66 mm (range, 1.52-3.55 mm), and the AWT of pathologic specimens averaged 2.36 ± 0.75 mm (range, 1.51-4.16 mm). The percentage difference between pathologic specimens and CTA-determined AWT was 9.5% ± 4.1% (range, 1.8%-16.7%). The correlation between image-based measurements and pathologic measurements was high (R = 0.935). The 95% limits of agreement computed by Bland-Altman analysis fell within the range of -0.42 and 0.42 mm. Semiautomated analysis of CTA images can be used to accurately measure regional and patient-specific AWT, as validated using pathologic ex vivo human aortic specimens. Descriptions and reconstructions of aortic aneurysms that incorporate locally resolved wall thickness are feasible and may improve future attempts at biomechanical analyses.

Color image segmentation based on different color space models using automatic GrabCut.

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.

A handheld device for leaf area measurement

Advanced electronic technology makes handheld devices (HHD) suitable for leaf area (LA) measurement with image processing techniques. This article presents the development of an HHD applied to measure LA through image processing. The procedure is essentially a module of mobile phone software developed in Java ME that uses a HHD touch panel monitor to estimate LA. The system is composed of a control module to run specially developed software, an ultrasonic ranging module to calculate the plane distance between the leaf and the HHD, and an inclination angular measuring module to determine the plane inclination angle between the leaf and the HHD. The image processing operations on each image are as follows: semi-automatic image segmentation, binarization, noise filtering, and LA calculation. The measurement accuracy of the HHD surpasses 0.005cm2, and the cost is less than one tenth of a traditional machine vision system. Squares of 400mm2 and circles of 314.15mm2 were used to test the effects of distance and inclination angle of the HHD. The distance between the HHD and objects with inclination angle at 0°, 5°, 10° and 30° was set to 150mm and between distances of 200 and 800mm at 100-mm intervals. The results showed that the deviations were in the range of −0.62% to 0.79% at the same inclination angle. There were distinguishable errors between different angles by 5°. Leaf samples of tomato, eggplant, and maple representing varied shapes and sizes were used to compare the measurement performance of the HHD and an existing leaf area meter (Li-3100; LICOR, Lincoln, NE, USA). The results indicate that the HHD was an accurate device for LA measurement when the distances were in the range of 300–600mm; especially at the 400-mm point, the maximum error of the HHD compared with Li-3100 was 0.455%, and the minimum error was 0.04%. Overall the result indicated that the developed image processing method using the HHD provided a feasible non-destructive alternative to measure LA. This new technology enables agriculture and forestry workers to conveniently, accurately, and reliably estimate the area of leaves without using expensive instruments.

Magnetic Resonance Imaging-Based Target Volume Delineation in Radiation Therapy Treatment Planning for Brain Tumors Using Localized Region-Based Active Contour

To evaluate the clinical application of a robust semiautomatic image segmentation method to determine the brain target volumes in radiation therapy treatment planning. A local robust region-based algorithm was used on MRI brain images to study the clinical target volume (CTV) of several patients. First, 3 oncologists delineated CTVs of 10 patients manually, and the process time for each patient was calculated. The averages of the oncologists' contours were evaluated and considered as reference contours. Then, to determine the CTV through the semiautomatic method, a fourth oncologist who was blind to all manual contours selected 4-8 points around the edema and defined the initial contour. The time to obtain the final contour was calculated again for each patient. Manual and semiautomatic segmentation were compared using 3 different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. A comparison also was performed between volumes obtained from semiautomatic and manual methods. Manual delineation processing time of tumors for each patient was dependent on its size and complexity and had a mean (±SD) of 12.33 ± 2.47 minutes, whereas it was 3.254 ± 1.7507 minutes for the semiautomatic method. Means of Dice coefficient, Hausdorff distance, and mean absolute distance between manual contours were 0.84 ± 0.02, 2.05 ± 0.66 cm, and 0.78 ± 0.15 cm, and they were 0.82 ± 0.03, 1.91 ± 0.65 cm, and 0.7 ± 0.22 cm between manual and semiautomatic contours, respectively. Moreover, the mean volume ratio (=semiautomatic/manual) calculated for all samples was 0.87. Given the deformability of this method, the results showed reasonable accuracy and similarity to the results of manual contouring by the oncologists. This study shows that the localized region-based algorithms can have great ability in determining the CTV and can be appropriate alternatives for manual approaches in brain cancer.

Visceral abdominal obesity measured by CT scan is associated with an increased risk of Barrett's oesophagus: a case-control study

ObjectiveAbdominal obesity has been associated with increased risk of Barrett's oesophagus (BE) but the underlying mechanism is unclear. We examined the association between visceral adipose tissue (VAT) and subcutaneous adipose...

An Image Skeletonization‐Based Tool for Pollen Tube Morphology Analysis and PhenotypingF

The mechanism underlying pollen tube growth involves diverse genes and molecular pathways. Alterations in the regulatory genes or pathways cause phenotypic changes reflected by cellular morphology, which can be captured using fluorescence microscopy. Determining and classifying pollen tube morphological phenotypes in such microscopic images is key to our understanding the involvement of genes and pathways. In this context, we propose a computational method to extract quantitative morphological features, and demonstrate that these features reflect morphological differences relevant to distinguish different defects of pollen tube growth. The corresponding software tool furthermore includes a novel semi-automated image segmentation approach, allowing to highly accurately identify the boundary of a pollen tube in a microscopic image.

Spinal Cord Atrophy Correlates with Disability in Friedreich’s Ataxia

Although Friedreich's ataxia is characterized by spinal cord atrophy, it remains to be investigated the possible correlation of such atrophy with clinical disability and genetic parameters. Thirty-three patients with Friedreich's ataxia and 30 healthy controls underwent MRI on a 3 T scanner. We used T1-weighted 3D images to estimate spinal cord area and eccentricity at C2/C3 level based on a semi-automatic image segmentation protocol. We quantified severity of ataxia with the Friedreich ataxia rating scale (FARS). Mean cord area in Friedreich's ataxia was smaller than in controls (38 vs 67.9 mm(2), p < 0.001). In contrast, mean cord eccentricity was significantly higher in Friedreich's ataxia when compared to the controls (0.82 vs 0.76, p < 0.001). There was a significant correlation between cord areas and the FARS scores (r = -0.53, p = 0.002). Cord damage in Friedreich's ataxia results in atrophy combined with flattening. Cord area is associated to clinical disability and might be useful as a biomarker in the disease.

Editorial: Resection and survival

Sankar et al.15 retrospectively review their single-institution experience with oliogodendrogliomas that enhance preoperatively to determine if the amount of postoperative residual enhancing volume has prognostic importance. They use a semiautomated, free software tool that provides quantitative volumetric measurements of residual enhancing volume and demonstrate enviable intraand interobserver reliability. Their analysis shows that reduced postoperative residual enhancing volume and a greater resection of enhancing tissue are associated with longer overall survival times. Of note, the amount of resection that was required for this prognostic benefit was greater than 95%. Computed methods of image segmentation for assessing enhancing brain tumor were first reported in the late 1990s and have been increasingly utilized in recent years.8,9, 11,16,18 Compared to measurements of tumor on 2-dimensional MR images, which form the basis of the RECIST17 and Macdonald10 criteria, computed image segmentation provides a more precise and reproducible measure of tumor volume from MRI studies.4 A precise tumor volume measurement is critical for studies, such as this one, that evaluate tumor volume as a prognostic factor. Sankar and colleagues’ findings15 are supported by a prior study that show volumetric measurements correlate better with prognosis compared to 1-dimensional (RECIST7) and 2-dimensional (Macdonald10) measurements for recurrent malignant glioma.5 Sankar et al. utilize Medical Image Processing and Visualization (MIPAV) software that is made freely available by the National Institutes of Health, but there are multiple other methods of semiautomated image segmentation utilized by several different software applications.3,11,12 Com mon to all is the process of selecting representative seed points within the tumor and editing the images to include or exclude improperly classified regions. We are in desperate need of a method of determining residual disease that is highly reproducible and has low interobserver variability, even among users of variable experience levels. Malignant primary brain tumors are often irregular in shape, cystic, or have satellite lesions that make traditional 1or 2-dimensional methods difficult, if not impossible, to use. Moreover, after resection, thin rims of enhancement may be seen which cannot be represented accurately using these other methods.9 An assessment of tumor response or progression in the context of these small rims of enhancement is impossible. This is important because tumor response is now being accepted as a criterion for drug approval by the FDA.6,13,14,21 In addition, if we could better and more quickly assess tumor progression, we might be able to stop ineffective treatments earlier and better serve our patients. There still remain some problems with the method proposed by Sankar et al.,15 however. First of all, the signal from blood, again not uncommonly seen in postoperative resection cavities, would be difficult to differentiate from tumor enhancement using their approach. In addition, changes in gadolinium contrast enhancement are not linear, so better methods of quantitating residual tumor as it relates to gadolinium contrast enhancement are still needed. Finally, in the age of “pseudo-progression”1,2 and “pseudo-response,”19 we will need to develop additional criteria above and beyond imaging enhancement to assess tumor volume and progression.15,20 (http://thejns.org/doi/abs/10.3171/2011.10.JNS111437)

A processing work-flow for measuring erythrocytes velocity in extended vascular networks from wide field high-resolution optical imaging data

Comprehensive information on the spatio-temporal dynamics of the vascular response is needed to underpin the signals used in hemodynamics-based functional imaging. It has recently been shown that red blood cells (RBCs) velocity and its changes can be extracted from wide-field optical imaging recordings of intrinsic absorption changes in cortex. Here, we describe a complete processing work-flow for reliable RBC velocity estimation in cortical networks. Several pre-processing steps are implemented: image co-registration, necessary to correct for small movements of the vasculature, semi-automatic image segmentation for fast and reproducible vessel selection, reconstruction of RBC trajectories patterns for each micro-vessel, and spatio-temporal filtering to enhance the desired data characteristics. The main analysis step is composed of two robust algorithms for estimating the RBCs' velocity field. Vessel diameter and its changes are also estimated, as well as local changes in backscattered light intensity. This full processing chain is implemented with a software suite that is freely distributed. The software uses efficient data management for handling the very large data sets obtained with in vivo optical imaging. It offers a complete and user-friendly graphical user interface with visualization tools for displaying and exploring data and results. A full data simulation framework is also provided in order to optimize the performances of the algorithm with respect to several characteristics of the data. We illustrate the performance of our method in three different cases of in vivo data. We first document the massive RBC speed response evoked by a spreading depression in anesthetized rat somato-sensory cortex. Second, we show the velocity response elicited by a visual stimulation in anesthetized cat visual cortex. Finally, we report, for the first time, visually-evoked RBC speed responses in an extended vascular network in awake monkey extrastriate cortex.