Facing the issues of deep grey matter segmentation in MS
de Sitter, A.
2020
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de Sitter, A. (2020). Facing the issues of deep grey matter segmentation in MS.
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Appendix
volume and structure of interest volume. This suggests that MS pathology may contribute to the impaired performance.
In Chapter 3 we investigated several aspects of the WM lesions in MS, divided over two sub-chapters. In Chapter 3.1 we discussed the performance of five automated WM lesion segmentation methods on a multi-center MS dataset (70 MS subjects). On the 2D fluid attenuated inversion recovery (FLAIR) images, manual lesion segmentation was performed and the segmentations of five automated methods (Cascade, LST-LGA, LST-LPA, Lesion TOADS and kNN-TTP) were compared to the manual outlines. Both volumetric (ICC) and spatial agreements (DSC and false positive and false negative volumes) were analysed. Furthermore, analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase, in order to evaluate the performance of the method on ‘unseen’ centers. We concluded that the performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.
In Chapter 3.2 we developed a lesion simulation method (LESIM) to improve objective investigations of the effects of WM lesions on image analyses methods and to facilitate the development of segmentation methods that are robust to the presence of WM lesions. The LESIM software simulates lesions from an MS patient into a 3D T1-weighted (3DT1) image of a healthy control (HC), which results in a modified HC 3DT1 image with realistic lesions. We evaluated LESIM by visual inspection as well as a quantitative analysis of the effect of simulated lesions on FSL-SIENAX GM segmentations. We concluded that LESIM is a new, robust, and flexible tool for reliable WM MS lesion simulation that produces realistic lesions in healthy control images. Moreover, we showed that the simulated WM lesions have the expected effect on GM segmentation using FSL-SIENAX.
In Chapter 4 we addressed the issue of open science in the field of neuro-radiology. Firstly, by assessing the impact of facial features removal on clinically relevant outcome measurements (Chapter 4.1) and secondly, by developing and evaluating a standardized protocol for manual delineations of dGM structures (Chatper 4.2).
So, in Chapter 4.1 we investigated if removing facial features would affect subsequent automated image analyses. To do so, we tested the effect of three facial features removal methods (QuickShear, FaceMasking, and Defacing) on automated image analyses methods that give clinically relevant outcome measurements. We used three datasets of different diseases: Alzheimer’s Disease, MS, and patients with a glioblastoma. Therefore, we also used three different clinically relevant outcome measurements, respectively, normalized brain volume, white matter lesion volume and tumor volume. Differences between outcomes
English Summary
The aim of this study was to improve the measurement of deep grey matter (dGM) atrophy in multiple sclerosis (MS) with the use of magnetic resonance imaging (MRI). For this we started with discussing the challenges of measuring GM in MS in Chapter 1. One of the conclusions was that more accurate automated segmentations methods are needed. Therefore, in Chapter 2, we evaluated the relation of the performance of well-established automated segmentation software with MS pathology. In Chapter 3 we addressed the white matter (WM) lesions, as WM lesions play an important role in diagnosis of MS and also affect brain image analyses in MS. To further stimulate methodological improvements in measurement of dGM atrophy in MS, we discussed in Chapter 4 how to improve open science in this field. Lastly, in Chapter 5 we developed an MS-specific automated segmentation software, MS-SMART, and an open reference dataset.
In Chapter 1 we discussed the urgent challenges of measuring grey matter (GM) atrophy in MS, distinguishing two main fields; i. pathology, physiology, and treatment effects and ii. measurement challenges. We discussed in more detail the pathological substrate, evolution of GM atrophy, influence of physiological variability, and evaluation of treatment response. Regarding technical measurement challenges, we discussed the influence of WM lesions on the measurement of GM atrophy. Moreover the influence of atrophy itself, the influence of other MS pathology, and technical variability. For every discussion point, we provided specific recommendations (summarized in Box 1 of Chapter 1) to improve measurements and interpretation of GM atrophy in individual MS patients. Two of these recommendations provide the basis for the rest of this thesis; the need of a public available reference data set and improvement of segmentation methods for MS.
In Chapter 2, we investigated the performance of existing automated dGM segmentation methods compared to a manual reference. Moreover, we evaluated whether there was a relation of the performance of those automated dGM segmentation methods with WM lesions and GM volume. We evaluated four different automated segmentation methods (FSL-FIRST, FreeSurfer, GIF and volBrain) on a multi-center dataset (21 MS subjects and 11 healthy controls). The performance of the methods was evaluated to manual reference on both volumetric (intraclass correlation (ICC)) and spatial (dice similarity coefficient (DSC)) agereement. The relation between segmentation accuracy of the methods, as expressed by their DSC with the manual outlines, and the global and local lesion volumes, region of interest volume, and normalized brain volume, was assessed. We concluded that existing automated methods have impaired performance on data of MS subjects, specifically, that the accuracy of the segmentations is reduced. Moreover, it was observed that performance generally deteriorated with higher lesion volume, and with lower normalized brain
Nederlandse Samenvatting
Achtergrond
MS
In Nederland wordt ongeveer 1 op de 1000 mensen gediagnostiseerd met Multiple Sclerosis (MS). Meestal krijgen ze de diagnose tussen de leeftijd van 20 en 50 jaar en het komt vaker voor bij vrouwen dan mannen (1). MS is een neurologische aandoening van het centrale zenuwstelsel en de symptomen en ziekteverloop zijn zeer verschillend voor elke individu. De meest voorkomende symptomen bij MS zijn vermoeidheid, zwakke en stijve spieren, oogproblemen (zichtverlies, dubbel of wazig zien), tintelingen en gevoelsstoornis in armen en benen, spraakproblemen en balansproblemen. Recentelijke studies laten daarnaast zien dat door MS ook geheugen- en concentratieproblemen kunnen ontstaan (2, 3).
De oorzaak van MS is nog niet bekend maar wel is duidelijk dat MS wordt gekenmerkt door focale inflammatoire, demyeliniserende laesies in voornamelijk de witte stof (WS) en degeneratie en volumeverlies van grijze stof (GS) in het centrale zenuwstelsel (de hersenen en ruggenmerg) (6, 7). Deze veranderingen kunnen in vivo zichtbaar worden gemaakt door gebruik van Magnetic Resonance Imaging (MRI) (8, 9).
De degeneratie en volumeverlies van de GS wordt vaak GS atrofie (krimping) genoemd en kan bij MS zowel corticaal als subcorticaal plaatsvinden (2, 7, 10, 11). De subcorticale atrofie treedt op in de diepe GS (dGS) structuren zoals nucleus caudatus, putamen en thalamus (3, 11-15). De precieze oorzaak en gevolgen van atrofie van de (d)GS is helaas niet bekend en daarom stellen verschillende onderzoeksgroepen voor dat er meer studies moeten komen naar de oorzaak en de impact van dGS atrofie bij MS (16, 17).
MRI
MRI kan gebruikt worden om de laesies en atrofie in vivo zichtbaar te maken en speelt daarom een belangrijke rol in diagnosticeren van en onderzoek naar MS. Om MS te diagnosticeren kijken artsen naar de combinatie van de hoeveelheid WS laesies en verloop van de ziekte (klinische terugvallen) in een bepaalde periode. Er zijn duidelijke criteria en richtlijnen opgesteld voor de diagnose door het medische veld (18, 19). De richtlijnen adviseren ook dat conventionele MRI technieken worden gebruikt voor de diagnose van MS, dus T1-gewogen en T2-gewogen beelden (fluid-attenuated inversion recovery (FLAIR) of proton density (PD)). Door het gebruik van deze beelden kunnen de WS laesies goed zichtbaar worden gemaakt en kan de GS en WS goed worden onderscheiden. In Figuur 1 zijn duidelijke onderscheidbare MS WS laesies weergegeven op een axiale MRI slice van zowel een T1-gewogen beeld en FLAIR beelden. In Figuur 2, een T1-gewogen beeld is zichtbaar met daarbij de duidelijk onderscheidbare dGS van de WS. Door op het obtained from images from which facial features were removed and those obtained from full
images were assessed by quantifying the intra-class correlation coefficient (ICC) for absolute agreement, and by testing for systematic differences using paired t-tests. We conclude that all three outcome measures were affected, although all differently, by the facial features removal methods. This included both failures of analyses methods and altered values for the outcome measures, including both “random” variation and systematic differences.
In Chapter 4.2 we discussed the development and evaluation of a manual segmentation protocol of dGM structures in MS. Next, we evaluated the accuracy of FASTSURF, a semi-automated segmentation method, in which sparse delineations serve as input. The standardized protocol was specifically developed for manually tracing dGM structures on 3D T1-weighted MRI scans of MS patients, by neurologists and neuroradiologists with broad experience in the field of MS and MRI. Aanatomical definitions were specified for each structure and alongside these landmarks, strict guidelines on how to recognize the outermost edges of the structures on orthogonal planes were described. To evaluate the protocol, three raters delineated dGM structures bilaterally on 3D-T1-weighted multi-center MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreements? were assessed through volumetric (ICC) and spatial (JI and CIgen) agreement. Segmentations made with FASTSURF were also evaluated in terms of both volumetric and spatial agreement. We showed that raters achieved good to excellent intra- and inter-rater agreement and that these agreements were similar with use of FASTSURF. We concluded that the dGM manual segmentation protocol showed good reproducibility within and among raters. Moreover, this protocol could be combined with FASTSURF to produce a reference set of dGM structures with a lower workload.
In Chapter 5 we discussed the development of an MS-specific dGM automated segmentation method. MS-SMART is an open source automated segmentation method and is an atlas-based approach. The atlases for MS-SMART were manual outlined on 120 (100 MS subjects and 20 healthy controls) T1 MR images with use of the protocol developed in Chapter 4.2. The use of MS-specific atlases (images and labels) could help reduce the influence of MS pathology during alignment of the atlases to the target (input) image. In total, 60 images were used as an? atlas and training set for SMART and the other 60 were used for the evaluation of SMART and two well-established automated segmentation methods (FSL-FIRST and FreeSurfer). Evaluation was performed on both volumetric (ICC) and spatial (DSC) agreement compared to the manual outlines. We concluded that SMART outperformed the two well-established methods on this MS data set. However, we expect that with use of the shared atlas set and software code of SMART more methodological improvements in segmentation of dGM structures in MS could be made.
Figure 1: Een axiale snede van een T1-gewogen en FLAIR beeld met duidelijk onderscheidbare MS witte stof laesie.
Figure 2: Een axiale snede van T1-gewogen beeld, met drie diepe grijze stof structuren en witte stof aangegeven.
T1-gewogen beeld de dGS structuren plak voor plak uit te lijnen, ook wel segmenteren genoemd, kan het volume van de structuren worden berekend. Door het berekenen van volumeverschil van twee of meerdere segmentaties op beelden van 1 persoon na verloop van tijd, kan de hoeveelheid dGS atrofie worden berekend. Het segmenteren kan handmatig of automatisch worden gedaan.
Open science en privacy van patiënten
Open science is een ontwikkeling in het openbaar maken van je onderzoeksdoelen, resultaten en artikelen, en wordt afgelopen jaren aangemoedigd door vele tijdschriften en onderzoeksgroepen. Open science behelst ook het delen van ruwe data, bijvoorbeeld de gebruikte patiëntendata (demografische en klinische data, MRI). Het delen van deze data maakt niet alleen meta- en mega-analyse mogelijk, maar ook methodologische studies en/of samenwerkingen tussen groepen. Het delen van patiëntendata opent echter ook een ethische discussie. Zonder toestemming van de patiënt zou de data namelijk volledig anoniem moeten worden gedeeld. Dit betekent voor het delen van MRI beelden van de hersenen dat naast het verwijderen van metadata (gegevens zoals naam, geboortedatum, burgerservicenummer en/of registratienummer van ziekenhuis) ook gezichtskenmerken van het beeld verwijderd moeten worden. Door het goede huid-tot-luchtcontrast en ruimtelijke resolutie van MRI is het namelijk mogelijk om van een 3D MRI beeld iemand te herkennen (25-29). Het verwijderen van gezichtskenmerken kan op verschillende manieren en helpt om de data meer anoniem te maken, maar voor correct delen van data is het ook nodig om overeenkomsten met patiënten en centra op te stellen.
In Hoofdstuk 3.2 hebben we een laesie simulatiesoftware (LESIM) ontwikkeld. Door laesie te simuleren in MRI beelden van gezonde controles kunnen we op een meer objectieve manier de effecten van WS laesies op beeld analyse methods analyseren. De software kan helpen bij het ontwikkelen van automatische segmentatiemethodes die geen effect hebben van WS laesies. LESIM simuleert laesies van T1-gewogen of FLAIR beelden van mensen met MS in T1-gewogen beelden van gezonde controles. We hebben de software geëvalueerd door zowel visueel het resultaat te beoordelen als het effect van gesimuleerde laesies op GS segmentatie met FSL-SIENAX te evalueren. De verwachting is dat door de gesimuleerde laesies de GS segmentatie verandert, zoals door echte laesies ook gebeurt. De evaluatie van de software laat zien dat LESIM een nieuwe, goedwerkende software is voor het simuleren van realistische WS laesies en dat de gesimuleerde laesies het verwachte effect hebben op GS segmentatie van FSL-SIENAX.
Hoofdstuk 4 staat in het teken van de open science en privacy van patiënten. Eerst hebben
we in Hoofdstuk 4.1 gekeken naar de invloed van verwijderen van gezichtskenmerken van een MRI op beeldanalyse methodes. Met beeldanalyse methodes kunnen klinische maten gemeten worden, die voor diagnose en/of onderzoek naar verschillende ziektes kunnen worden gebruikt. In deze studie hebben we niet alleen naar MS gekeken maar ook naar de ziekte van Alzheimer en mensen met een glioblastoma (hersentumor). De drie gebruikte klinische maten waren (respectievelijk) WS laesievolume, genormaliseerd breinvolume en volume van de tumor. Door de gezichtskenmerken te verwijderen met drie verschillende methodes (QuickShear, FaceMasking en Defacing) konden we het verschil meten tussen gemeten klinische waarden, op beelden met en zonder gezichtskenmerken. We hebben het verschil gekwantificeerd met de intraclass correlatie coëfficiënt (ICC) en door te testen voor een systematisch verschil met gepaarde t-test. We zagen dat alle drie de klinische maten anders waren na het verwijderen van de gezichtskenmerken ten opzichte van de scans met gezichtskenmerken. Dit heeft als gevolg dat in het onderzoeksveld goed moet worden nagedacht over hoe data kan worden gedeeld zonder dat de privacy van patiënten in gevaar is, maar dat de analyses betrouwbaar blijven.
In Hoofdstuk 4.2 hebben we een protocol gemaakt en geëvalueerd voor het handmatig segmenteren van dGS structuren. Het protocol is ontwikkeld door neurologen en neuro-radiologen en is bedoeld voor het segmenteren van de thalamus, putamen en caudates nucleus op 3DT1 beelden van mensen met MS. Door gebruik van anatomische definities voor elke structuur konden er duidelijke richtlijnen worden opgesteld om de structuren te segmenteren. Drie raters, ook wel tekenaars, hebben op 35 beelden (23 van mensen met MS en 12 gezonde controles) de drie structuren zowel links als rechts
Doel en overzicht resultaten
Met deze studie wilden we het meten van de dGS atrofie bij MS verbeteren. Daarvoor hebben we eerst onderzocht wat de problemen en uitdagingen zijn bij het meten van GS atrofie bij MS, zie Hoofdstuk 1. We verdeelden de problemen en uitdagingen in twee groepen; i. de uitdagingen door pathologie, fysiologie en effect van behandelingen en ii. de uitdagingen door het meten zelf, dus software- en hardware-uitdagingen. In
Hoofdstuk 1 geven we voor elke uitdaging aanbevelingen om de meting en interpretatie
van GS atrofie te verbeteren. Twee van deze aanbevelingen zijn de basis van deze studie geworden: de noodzaak van een openbare referentieset met handmatige segmentaties van dGS structureren van mensen met MS en verbeteringen van automatische segmentatiemethodes voor dGS van mensen met MS.
Om de automatische methodes te verbeteren hebben we in Hoofdstuk 2 onderzocht hoe bestaande segmentatie methodes werkten en hun prestatie vergeleken met handmatige segmentaties. Daarnaast hebben we een mogelijke relatie onderzocht tussen de prestatie van de automatische methodes en de volledige en lokale WS laesies volume, de dGS volumes en het genormaliseerde hersenvolume. In totaal hebben we vier automatische segmentatiemethodes geëvalueerd (FSL-FIRST, FreeSurfer, GIF en volBrain) op een dataset van verschillende centra (21 mensen met MS en 11 gezonde controles). De methodes zijn geëvalueerd met de handmatige segmentaties op zowel volume (intra class correlatie (ICC)) als ruimtelijke overeenkomsten (dice similarity coefficient (DSC)). De resultaten laten zien dat de bestaande automatische methodes minder goed werken op data van mensen met MS dan op data van de gezonde controles. Daarnaast zagen we dat de automatische methodes vooral minder goed presenteren als het laesievolume hoger wordt en als het genormaliseerde hersenvolume en dGS volume lager worden. Dit suggereert dat MS pathologie voor de slechtere prestatie van automatische segmentaties zorgt in bij MS data.
Hoofstuk 3 staat in teken van de WS laesies bij MS. In Hoofdstuk 3.1 onderzochten we
de werking en prestatie van automatische WS laesie segmentatiemethodes op een multi-center MS dataset (70 mensen met MS). Op de 2D FLAIR beelden zijn de WS laesies zowel handmatig als met vijf automatische methodes gesegmenteerd (Cascade, LGA, LST-LPA, Lesion TOADS and kNN-TTP). De automatische methodes werden vergeleken met de handmatige segmentaties op zowel volume (ICC) als ruimtelijke overeenkomst (DSC en vals-positieve, en vals-negatieve volumes). Verder onderzochten we, met gebruik van ‘leave-one-center-out’ in het trainingsgedeelte, of methodes opnieuw getraind moeten worden voor een nieuw centrum. We zagen dat de methodes nog niet goed genoeg zijn voor klinisch gebruik maar de methodes zijn wel robuust tegen de nieuwe dataset van een (nieuw) centrum.
Biography
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met volume (ICC) en ruimtelijke overeenkomst (JI en CIgen) laat zien dat het protocol zorgt voor goede reproduceerbaarheid tussen en door raters. Daarnaast hebben we onderzocht of het protocol ook gebruikt kon worden in samenwerking met FASTSURF, een semiautomatisch segmentatiemethode. We zagen dat FASTSURF ook zorgde voor hoge reproduceerbaarheid tussen en door raters en dat FASTSURF even accuraat was als handmatige segmentaties. We concludeerden dus dat het protocol gebruikt kan worden voor het ontwikkelen van volledig handmatige segmentaties of segmentaties met FASTSURF.
Het laatste hoofdstuk, Hoofstuk 5, staat in teken van het ontwikkelen van een MS-specifiek automatisch dGS (nucleus caudates, putamen en thalamus) segmentatiemethode. We ontwikkelden MS-SMART als een open source methode zodat andere groepen de methode kunnen gaan gebruiken, testen en verder ontwikkelen. MS-SMART maakt gebruik van handmatig gesegmenteerde beelden die samen met de MRI beelden ‘atlassen’ worden genoemd. De segmentaties zijn gedaan via het protocol ontwikkeld in Hoofdstuk
4.2. In totaal is voor deze studie op 120 beelden (100 van mensen met MS en 20 gezonde
controles) gesegmenteerd. Hiervan zijn 60 beelden gebruikt als atlas voor training van MS-SMART en de andere 60 beelden voor het evalueren van MS-SMART en twee vaak gebruikte automatische segmentatiemethodes (FSL-FIRST en FreeSurfer). Voor evaluatie van de methodes keken we naar volume (ICC) en ruimtelijke (DSC) overeenkomst met de handmatige segmentaties. We zagen dat MS-SMART beter presteerde dan FSL-FIRST en FreeSurfer maar verder onderzoek en ontwikkeling zijn nodig om MS-SMART verder te verbeteren. We hopen dat door het openbaar maken van de softwarecode en de atlassen van MS-SMART andere groepen ook worden aangezet om dGS segmentaties bij MS te verbeteren.
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