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  • 1. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 1 Automated Measurement of Brain Volume in Patients after Aneurysmal Subarachnoid Hemorrhage Anne Kaspers, Biomedical Image Sciences, University Medical Centre Utrecht Abstract—Accurate and precise brain segmentations of cerebral abnormalities present in patients after aSAH, likeMagnetic Resonance (MR) brain images from patients after enlarged ventricles. k-Nearest Neighbor-based probabilisticaneurysmal subarachnoid hemorrhage (aSAH) are hard to segmentation (kNN) [8] is a supervised pattern recognitionacquire by an automated routine due to presence of variouscerebral abnormalities, like enlarged ventricles. Available method which can perform precise and accurate brain volumeroutines neither dealt with theses abnormalities nor were suited measurement [7], for which training data can be obtained fromfor MR images with high magnetic field strength or used different high resolution MR brain scans containing variety oftechniques with limited accuracy and precision. In order toperform accurate and precise brain volume measurements for 3 cerebral abnormalities.T aSAH MR images, we created a new routine in which we tried In this study we aimed therefore to design a new, automaticto deal with these cerebral abnormalities. Measurements of routine for quantification of cerebral structure volumes inintracranial volume, total brain, lateral ventricles and peripheral patients after aSAH, based on kNN using manually segmentedcerebrospinal fluid were performed on T1 and T2 weighted MRimages of 39 patients and 25 control participants using k-Nearest MR image training data.Neighbor (kNN) classification. Evaluation showed a fractionalSimilarity Index (fSI) of 0.98, 0.93 and 0.92 for respectively intra-cranial volume, total brain and lateral ventricles, which areequally good as the inter-observer results. II. MATERIALS AND METHODS A. Data Index Terms—Aneurysmal Subarachnoid Hemorrhage; k-Nearest Neighbor classification; Magnetic Resonance imaging;Segmentation For training 10 and for validation 12 scans of patients after aSAH and of age- and sex-matched control participants were included, which were obtained between 2005 and 2007. I. INTRODUCTION Patients who were screened on aneurysmata were included as control participants.A NEURYSMAL SUBARACHNOID HEMORRHA- Patients were excluded if they had additional aneurysms GE (aSAH) is a type of stroke, caused by a ruptured treated with neurosurgical clips that either contained intracranial aneurysm [1]. The annual incidence of a ferromagnetic material or were located less than 20 mm fromnon-traumatic aSAH varies from 6 - 8 cases per 100,000 the coiled aneurysm, had a cardiac pacemaker, wereperson-years [2]. Almost half died within thirty days [3] while claustrophobic or younger than 18 years [9].almost half of the survivors suffered from significant cognitive MRI scans were acquired on a 3T Philips magneticand neurological or cognitive deficits after a year [4]. It is resonance imaging system using a standardized protocol (24assumed that the size of neuropsychological deficits, contiguous slices, voxel size: 0.45 × 0.45 × 4.0 mm) andcommonly detected after treatment of ruptured intracranial consisted of an axial T1-weighted (repetition time in ms [TR]:aneurysms is associated with the loss of cerebral volume [5]. 500, echo time in ms [TE]: 10) and T2-weighted sequenceStudy by Bendel showed enlargement of cerebrospinal fluid (TR: 3000, TE: 80).(CSF) and ventricular volume in patients after aSAH, usingthe technique of voxel-based morphometry (VBM) [6].However, the accuracy and precision of VBM is limited sinceits measurements are based on an average brain, which is not B. Image processingspecific for aSAH patients [7]. Existing routines, which arebased on training data of Magnetic Resonance (MR) brain Routine stepsimages, were not suited to measure significant volumedifferences in scans of patients after aSAH. This is partly In figure 1, all routine steps from provided images tobecause they were made for MR image data with too low resulting probability maps are schematically visualized.magnetic field strength, and partly because they lacked
  • 2. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 2 Fig. 1. Flow chart of the Volume Measurement Routine
  • 3. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 3 First, the T1-weighted image was rigidly registered to the threshold, were summed to get a basic mask (figure 2D).T2-weighted image by using Elastix [10]. To exclude remaining non-brain structures and fill holes, a To exclude hyper-intense non-brain structures like skull and number of morphological operations were performed. Anfatty tissue, a brain mask was created by an automated routine, erosion with a round, 11 voxels wide kernel separated non-based on the k-means algorithm [11], which used both the T1- brain structures from the brain. These structures were removedand T2-weighted image (figure 2A). The first non-empty slice by segmenting groups of attaching mask voxels, furtherwas used 5 times to get more hyper-intense background mentioned as blobs, and keeping only the largest blob.information for k-means clustering. A foreground mask was Dilation with the same kernel as used for erosion restored thecreated using k-means clustering with a small sample set, old borders (figure 2E). A set of 6 dilations with a round, 9previous to full k-means clustering (figure 2B). Scan voxels wide kernel filled holes while kept the shape of theinhomogeneities were corrected by a shading correction mask edge intact. The mask was brought back within itsalgorithm using a multiplicative 4th order correction model on original borders by 7 erosions with the same kernel (figureall voxels covered by the foreground mask [12]. In full k- 2F). A maximum of the brain mask with holes and the erodedmeans clustering, all shading corrected T1 and T2 intensities mask restored the old borders while holes remained filledwere taken as samples in a 2D feature space, which only (figure 2G). At the end of the routine 3 dilations with a 7contained intensity parameters. The algorithm tried to find 10 voxels wide, round kernel increased the margin to include allmeans, which minimized the sum of Euclidean distance of all CSF below the skull. Since only the cerebral volume wassamples to their nearest mean. Each voxel was classified to the important for our study, the cerebellum was manuallycluster number of their nearest mean, which resulted in 10 segmented (figure 2H).brain clusters and 1 background cluster, derived from the The T2 image and the registered T1 image were multipliedforeground mask (figure 2C). voxelwise by their corresponding mask including cerebellum To select clusters suitable for the brain mask, cluster and inhomogeneities were corrected [12], resulting in brainnumbers were counted for a fixed selection of approximately extracted shading corrected images, which were used for kNN1/3 of the voxels located in the center of the cluster image. classification (figure 1, processing routine).The 4 largest clusters and extra clusters, which size exceeded a As post-processing, small groups of attaching probabilities, Fig. 2. k-Means mask routine
  • 4. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 4further mentioned as blobs, were transferred from the lateral inclusion of a representative selection of all shading areas inventricles to the peripheral CSF probability map; only the the training data, which would enlarge the overlap of structurelargest blob was not transferred. Afterwards, a visual check samples in feature space. In figure 4, T1 and T2 weightedwas done to move back wrongly transferred blobs. intensities of samples from a training data patient with To remove as subcortical structures and cortical grey matter numerous parenchymal high-signal intensity lesions on T2-misclassified background outside the brain, the mask was weighted MRI are shown before and after inhomogeneityeroded 2 times with a round, 7 voxels wide, kernel and voxels correction. Both the T1 and T2 weighted image addedof subcortical structures and cortical grey matter outside the information, which showed the different range of structures oneroded mask were excluded. Infarcts, drain trajectories, the x- and y-axis. After correction, intensities of all structuresmeningiomas, etcetera, significantly diminished classification were more concentrated and distinctive. Cortical grey matter,outcome and were manually segmented and removed from the peripheral CSF and parenchymal lesion intensities were betterprobability maps. In figure 3, an example classification separated from each other while there was still overlapoutcome of one participant is shown. between subcortical structures and cortical grey matter, which could be explained by the unclear border in both the T1 and T2 weighted image. The effect of inhomogeneity correction to Routine choices cortical grey matter classification is shown in figure 5 for a participant scan with little and one with significant shading. In this study, volume measurements of subcortical After correction, cortical grey matter was better classified onstructures, cortical grey matter, peripheral CSF and lateral the shading area, which made the segmentation more uniform.ventricles were performed. Besides these structures, other To create a proper brain mask, we designed an automatedstructures were included in the masked area, further mentioned routine, based on the k-means algorithm [11]. It was extendedas background, which needed to be included in the training with cluster selection and a set of morphological operations todata to prevent misclassification. Assignment of all not fill holes, caused by exclusion of small clusters in the brain,classified voxels as background in the training data would while original borders were maintained. Parameters for clusterincorrectly assign partial volume brain structure voxels to the selection were determined by testing values close to thebackground. Assignment of only hypo-intense voxels as settings which were used in a study by Jongen [13] on ourbackground would lead to misclassification of hyper-intense training data. In contrast to the mask routine used by Jongen,background to closely located brain structures with similar we automated cluster selection by setting a cluster sizeintensity. Therefore, we put a manual selection of non-partial threshold, which provided good cluster selection for 9 of thehypo- and hyper-intense background in the training data. 10 training data images. After cluster selection, a large numberRemaining misclassified skull and fatty tissue classified as of small dilations, followed by one more number of smallsubcortical structures and cortical grey matter was removed if erosions was used instead of a large morphologic closing, toit was located within 6 voxels of the edge of the brain mask, fill large holes without loss of border detail. Holes close to theunder the assumption that only peripheral CSF could be border were filled while the original border was kept intact bylocated there. taking voxelwise the maximum of the unclosed mask and the The provided T1 and T2 weighted MR brain images closed, eroded mask.contained a shading artefact, which diminished intensity For a selection of participants, results of k-means and thehomogeneity for each brain structure. We applied Brain Extraction Tool (BET) were compared [14]. In normalinhomogeneity correction [12], assuming its effect to the cases BET performed similar to k-means, but in cases withclassification could be large since the orientation of the shaded large infarcts k-means performed better. In k-means we couldarea is different for each scan, which makes it hard to handle determine the number and selection of clusters to be classified.by kNN. Preventive removal of shading seemed better than Fig. 3. A registered T1 and T2 weighted image and corresponding kNN probability maps of subcortical structures, cortical grey matter, peripheral CSF and lateral ventricles.
  • 5. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 5 Fig. 4 A. Scatter plot of voxel intensities of the original T2W image relative to the registered original T1WFFE image of one patient from the training data. Five structures are indicated: subcortical structures (SCS), cortical grey matter (CGM), peripheral (per.) CSF, lateral (lat.) ventricles and parenchymal (par.) lesions B. Same for shading corrected intensities.This allowed us to include large infarcts and exclude hyper- C. Training data routineintense background. BET often considered infarcts as non-brain structures, which caused large gaps in the mask. Since a The training data consisted of non-partial volumelarger part of the patients after aSAH had infarcts (n = 40), we segmentations of 10 participant scans (JB). It is achose to use k-means instead of BET. representative selection of the dataset (Appendix A), All blobs in the lateral ventricles probability map, except the composed of scans of patients after aSAH and controllargest were transferred from the lateral ventricles to the participants, which varied in modified Rankin Scale [15] andperipheral CSF probability map, under the assumption that all size of the lateral ventricles. The segmentations containedlateral ventricle voxels attach to each other. However, this background and 4 brain structures: subcortical structures,assumption was not valid in all cases because of the large slice cortical grey matter, peripheral CSF and lateral ventricles. Forthickness. Manual adjustment was needed for some posterior all training data participants pre-processing was performedand inferior ventricle horns. Nevertheless, this operation was (section C). A fixed, random selection of 40% of the manuallyan easy way to get improvement. segmented structures and background was saved by their brain Since we were only interested in volume measurements of extracted shading corrected T1 and T2 weighted intensity andbrain structures in the cerebrum, we needed to segment the spatial parameters. The kNN algorithm could calculatecerebellum. However, presence of subcortical structures, distances in feature space to obtain structure probabilities ofcortical grey matter and peripheral CSF in both cerebrum and partial volume samples.cerebellum complicated kNN classification and search forbetter methods exceeded the project scope, so we segmentedthe cerebellum manually. Because the border betweencerebrum and cerebellum was unclear, specific segmentation D. Validation routinerules had to be defined to guaranty consistency. Right or left hemispheres were selected randomly throughout the brain from 12 participant scans of whom 6 were from the training data and 6 from other data. Subcortical structures, cortical grey matter, peripheral CSF and lateral
  • 6. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 6 Fig. 5. Example of an image with a significant shading artefact (top) and a small shading artefact (bottom) with their cortical grey matter classifications using SC training data on the SC image (middle) and using uncorrected training data on the uncorrected image (right).ventricles in these slices were manually segmented by 2 lateral ventricles were merged. The manual fraction for voxelobservers. They could indicate multiple structures per voxel. of resp. total brain and total CSF for a single observer areSo, in contrast to the training data, validation data also defined ascontained partial volume voxels. Since there were multiple structures per voxel, manualfractions could be computed, as well as for single as combinedobservers. Uniform distribution of structures and observercertainty was assumed for each voxel, since no informationabout the distribution was provided. For a single observer, themanual fraction for voxel and structure is defined as and .where is the binary value for voxel and structure of the observer and the number of structuresclassified in voxel by the observer. In order to enlarge the For combined observers, the average of the total brain andrange of manual fractions, uncertainty of both observers were total CSF were taken. The manual value of intracranialcombined. For combined observers, the manual fraction is volume is binary for a single observer, since it is 1 for allequal to the average of both observer manual fractions. structures and 0 for the background, and fractional for For calculation of the manual fraction of total brain, combined observers, for which the average of the binarysubcortical structures and cortical grey matter were merged, values of both observers were taken.and for the manual fraction of total CSF, peripheral CSF and
  • 7. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 7E. Evaluation where is the sum of minima of the The agreement of observer segmentations and the automatic reference and segmentation probabilities, equivalent to thesegmentation, acquired by kNN classification, and the inter- sum of true positives, is the sum of referenceobserver agreement, were measured by a variant of the Dice probabilities, equivalent to the sum of true positives and falsesimilarity index (SI) [16, 17] . The SI formula assumes binary negatives, is the number ofvalues for both the reference and the segmentation. It is voxels minus the maxima of the reference and segmentationdefined as probabilities, equivalent to the sum of true negatives, and is the number of voxels minus the sum of reference probabilities, equivalent to the sum of true negatives and false positives. The reference and segmented volume were determined by multiplication of and to the volume of 1 voxel in milliliters. The difference was examined to detect over- or under-segmentation of the automated structurewhere “Ref” denotes the volume of the binary reference, volumes.“Seg” is the volume of the binary segmentation, “Ref ∩ Seg” Inter-observer and routine fSI and sensitivity scores ofdenotes the volume of the intersection of the binary reference subcortical structures, cortical grey matter, peripheral CSF,and binary segmentation, is the sum over all voxels lateral ventricles, total brain, total CSF and intracranialin the binary reference, is the sum over all voxels, volume were analyzed. To investigate if inclusion of trainingwhere in the binary reference the intensity value equals 1 and data in the validation data improved validation scores, fSIidem for the binary segmentation. scores were compared for a validation set of only training data Because we calculated manual fractions for the observer to a validation set of non training data.segmentations, and kNN classification provided probabilisticsegmentations, the fractional Similarity Index (fSI) wasmeasured [18]. It is defined as III. RESULTS Table I shows the inter-observer validation results for all structures. Apart from peripheral CSF, fSI scores of all structures are good with a score of 0.82 for cortical grey matter and total CSF, 0.95 for lateral ventricles and total brain and even 0.98 for intracranial volume. Contrary to their highwhere is the manual fraction, computed for single fSI score, sensitivity of cortical grey matter is moderate with aobservers (formula 1) or combined observers (formula 2). score of 0.77.Notice that in case probabilistic values are substituted for Table II shows the routine validation results for allbinary values, the fSI formula is equal to the SI formula. The structures. Intracranial volume, total brain and lateralagreement of the probabilistic manual segmentations with the ventricles scored well with fSI scores of resp. 0.98, 0.93, 0.92automatic segmentation and the inter-observer agreement were and similar sensitivity scores. Subcortical structures scoredmeasured with the fSI. less with a fSI score of 0.83 and a sensitivity score of 0.88. Besides the fSI, also the sensitivity and specificity were Total CSF, cortical grey matter and peripheral CSF scoredmeasured, which are more common quality indicators and moderately with fSI scores of resp. 0.77, 0.76 and 0.71.therefore makes the validation outcome comparable to otherstudies. They are defined asand
  • 8. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 8 TABLE I TABLE II INTER-OBSERVER VALIDATION RESULTS ROUTINE VALIDATION RESULTS Tissue type Sensitivity Specificity fSI Tissue type Sensitivity Specificity fSI Subcortical structures 0.89 0.99 0.87 Subcortical structures 0.88 0.98 0.83 Cortical grey matter 0.77 0.99 0.82 Cortical grey matter 0.70 0.98 0.76 Peripheral CSF 0.87 0.99 0.77 Peripheral CSF 0.74 0.99 0.71 Lateral Ventricles 0.95 1.00 0.95 Lateral Ventricles 0.92 1.00 0.92 Total Brain 0.93 1.00 0.95 Total Brain 0.92 0.99 0.93 Total CSF 0.90 0.99 0.82 Total CSF 0.80 0.99 0.77 Intracranial 0.98 1.00 0.98 Intracranial 0.98 0.99 0.98 IV. DISCUSSION segmentation was not feasible, since parenchymal high-signal intensity lesions and lateral ventricles were both hyper-intense In this paper we proposed a kNN based routine to segment on T2-weighted MRI and closely located to each other, andsubcortical structures, subcortical grey matter, peripheral CSF occur on different locations and in different amounts.and lateral ventricles on 3T T1 and T2 MR brain images of Therefore, they were combined with subcortical structures topatients after aSAH. To measure subtle differences in brain which they belong anatomically.volumes, high accuracy and precision were required.Therefore, we based our routine on the kNN algorithm, whichis an accurate and precise method, and used accurate training B. Validation issuesdata of an expert and automated most routine steps for optimalprecision. The fSI scores of intracranial volume, total brain In order to fully exploit the observer segmentations, theyand lateral ventricles were good, while subcortical structures, were combined into manual fractions, which take partialtotal CSF, cortical grey matter and peripheral CSF scores were volume into account. Both observers got equal share, even iflower. one observer did not assign any structure. Information about the distribution of multiple structures in a voxel was not indicated by the observers, so we considered equal importance of all structures. For example, three structures in a voxel allA. Classification issues got a probability of 1/3, in case of one observer. In reality, one of the three structures could be dominant and should have a The low scores of cortical grey matter, peripheral and total higher probability. For all partial volume voxels whereCSF are partially explained by the slice thickness (4 mm), structures were not equally distributed, manual fractionswhich exceeded the thickness of cortical grey matter (2-4 mm) deviate, which caused lower classification scores. However,and peripheral CSF (± 2 mm) [19], which made it largelyconsist of partial volume. Subcortical structures and especiallycortical grey matter both have a lower fSI score than totalbrain. This is partly explained by the large overlapping areabetween subcortical structures and cortical grey matter, wherepartial volume correction caused rounding errors, and partlyby the perivascular spaces, which were misclassified ascortical grey matter (figure 6). Several studies showed that fluid attenuation inversionrecovery (FLAIR) images were more suitable for classificationof parenchymal high-signal intensity lesions on T2-weightedMRI since it showed them hyper-intense and ventricles hypo-intense [20]–[23]. In a study by Anbeek, its optimal SI scoredecreased from 0.81 to 0.63 when FLAIR images wereexcluded from training data, which consisted of inverserecovery (IR), proton-density (PD), T1 and T2 weighted Fig. 6. Example of perivascular spaces misclassified as cortical grey matter.images [24]. Because we did not have FLAIR images, good
  • 9. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 9we assumed that in a voxel, dominant structures will always strongly on the composition of the training data, in whichbe noticed by both observers and inferior structures could be cerebral abnormalities were included. Samples of the trainingmissed by one observer, which will compensate for some of data were consistently used by kNN for precise classification.the deviation. Because kNN effectively measured spatial and intensity Manual fractions could only take a limited number of values, distances in feature space, only a small training set of non-while kNN output had a wide range. Hence there was always partial voxels was enough to deal with partial volume. The k-an error margin added, which decreased our fSI scores. We means algorithm, which was used for brain mask creation, ischose not to threshold kNN output to the range of manual also simple and provides precise cluster images, underfractions because it would change results for validation assumption that sufficient samples were taken. With the use ofreasons, while the unadjusted results were used for volume our defined set of morphological operations, cluster imagesmeasurement. could be transformed into closed masks, which kept original Using fSI instead of SI is an improvement because it could borders unchanged. Hence, the core of our routine is clear anddeal better with partial volume. Probabilistic outcome of our simple so we could focus on application specific processingkNN routine did not have to be rounded and information of for improvement of kNN results. Apart from cerebellummultiple structures of both observers could be utilized segmentation, all steps in our routine were automated.effectively. However, fSI scores were not used in other studies Selection of appropriate training data may require lots ofso far and could therefore not be compared. Measurement of expensive man hours, although a study by Vrooman showedthe SI and fSI between observers was possible, since their that automatic training with kNN is possible and routine stepssegmentations are binary and could be transformed to need only little adaption for general use [25]. Hence, itsfractions. The relation of fSI to SI scores could therefore be application is feasible and additions and changes could beexamined. Generally, the fSI scores were lower than SI scores, tested without much human intervention.especially for structures with lots of partial volume, likeperipheral CSF and total CSF, because the SI formula did notcorrect for partial volume. Usually a SI of 0.80 or higher is D. Strengths and limitationsconsidered a good segmentation and given that fSI is probablystricter than SI, we considered the same for fSI. Compared to The strength of the present study is the usage of non-partialthe optimal SI values of the kNN based routine used by volume samples in the training data for kNN classification.Anbeek, which were based on PD, T1 and T2 weighted scans, Accuracy of brain volume was evaluated using small,the present routine scored similar and even higher for lateral representative manual segmentations, which contained partialventricles. This is true while fSI is stricter and PD weighted volume information, while other brain volume measurementimages were not included [24]. The high fSI score for lateral studies use binary manual segmentations. Precision of brainventricles could be explained by the larger ventricle volume of volume could be evaluated because data was selected from apatients after aSAH. Larger ventricles consist mostly of non- significant number of scans with variety of cerebralpartial voxels, which could better be classified than partial abnormalities. For optimal precision, a standardized scanningvolume voxels. An even lower optimal SI for cortical grey protocol was used for acquiring images of the data set.matter, compared to our fSI score, indicated that our routine Automated routine steps ensured consistency whereas manualdid not fail but performed well using the kNN algorithm and steps were consequently performed, like cerebellumthe provided imagery. segmentation. Validation scores of the single observers versus the A limitation of the present routine is that many cerebralautomatic routine were approximately similar as the combined abnormalities, like infarcts and perivascular spaces, could notobservers versus the automatic routine. Adding extra be processed automatically. However, we had accurate manualinformation of uncertainty did not improve the scores. Leaving segmentations of those cerebral abnormalities to our disposal,training data out of the validation data did not change the so this limitation did not hinder accurate brain volumescores significantly, which indicated good classification measurements. The small number of observers limited thequality for new participant scans. evaluation because only 6 different values could be assigned to the manual fractions, while kNN probabilities could have 100 different values, but it is still better than using binaryC. Application manual values. Present routine is based on the kNN algorithm, which candeliver precise and accurate results, while it is also simple andfast. Its quality depends apart from the quality of the images,
  • 10. MASTER RESEARCH ARTICLE OF ANNE KASPERS, BIOMEDICAL IMAGE SCIENSES, UNIVERSITY MEDICAL CENTRE UTRECHT 10 V. CONCLUSION A.2 Cross-sectional routine In this paper, we proposed an automated routine for brain For all participants in the SAH database, pre-processing was volume measurements on MR brain images from patients after performed as mentioned. In two cases, only 3 clusters were aSAH. We extended kNN classification with processing steps, taken in k-means and in 5 cases an extra cluster was added which we described and evaluated. Lateral ventricles, total when a good cluster image initially did not result in a good brain and intracranial volume, have good validation scores mask. For some masks, eyes were removed, moderate while structures with more partial volume scored worse. It imperfections were adjusted or k-means was performed with could be explained by validation limitations, since visual fewer clusters because of movement artifacts, infarcts, inspection showed good performance for structures with much bleedings or without clear reason. partial volume, like peripheral CSF. Post-processing on kNN probability maps were performed, where in 18 cases, one or two ventricle horns, which voxels did not attach to the lateral ventricles voxels, had to be VI. FUTURE PROSPECTS manually moved back from peripheral CSF to lateral ventricles. Most cerebral abnormalities present in patients after aSAH Automated segmented volumes of all structures were were manually segmented, but could be automated after more calculated by multiplication of the size of one voxel in study or under other conditions. For accurate automatic milliliters to the sum of all probabilities. For the validation cerebellum segmentation, sagittal images may be needed, data, the difference between the automated and manual since they show the border between cerebrum and cerebellum volume and the average volume for all validation participants clearer. Validation scores of structures with much partial were calculated. volume should increase with the number of observers, because The total volumes of structures were calculated by it makes the manual fraction more accurate. These multiplication of the sum of their probabilities to the voxel assumptions need to be addressed in further studies. volume in milliliters. The results of the probabilistic classification of all structures were visually checked for all participants, and incorrectly classified images were excluded. Also total brain APPENDIX A and total CSF volume were calculated. The mean and standard deviation of the total brain, total CSF, subcortical structures, A.1 Data cortical grey matter, peripheral CSF, and lateral ventricular volume were measured for patients after aSAH and control For cross-sectional volume measurements, 39 patients after participants. aSAH and 30 control participants from the COMET study were selected. Inclusion criteria were mentioned in chapter Materials and Methods, section Data. Additionally, control A.3 Cross-sectional volume measurements participants with symptomatic ischemia were excluded. One control participant had a large infarct because of a neurotrauma and 3 control participants had clinically manifest Table A.I shows the mean and standard deviation of infarcts. automated volume measurements for control participants and patients after aSAH. As expected, patients after aSAH had larger lateral ventricles and infarcts than control participants. TABLE A.I MEAN VOLUMES AND STANDARD DEVIATION OF VOLUMES IN PATIENTS WITH SAH AND CONTROL PARTICIPANTS Peripheral CSF Lateral ventricles Total brain Total CSF Intracranial Infarct1Control participants 232 ± 52.5 26.6 ± 10.6 978 ± 80.8 259 ± 57.4 1235 ± 125 1.10 [0.67, 1.53] Volume (ml)Patients with SAH 200 ± 40.4 48.0 ± 25.4 956 ± 112 248 ± 39.4 1194 ± 134 5.92 [1.49, 20.8] Volume (ml)Data are unadjusted mean brain volumes ± SD or 1 median infarct volumes and interquartile range
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