Fast Fully Automatic Myocardial Segmentation in 4D cine Cardiac Magnetic Resonance datasets

Sandro

Queirós

Computers meten de grootte en werking van het menselijk hart

Ziekten aan het hart zijn één van de belangrijkste oorzaken van hospitalisatie en sterfte in de wereld. Om een behandeling tijdig te kunnen starten of om de effecten van een behandeling goed op te volgen is het belangrijk om de grootte, de vorm en de vormverandering tijdens de pompende beweging van het hart te kennen. Hiervoor worden scanners gebruikt die toelaten het hart in beeld te brengen. Een erg nauwkeurige (maar relatief dure) technologie maakt beelden door te meten hoe (waterstof)atomen bewegen in veranderende magnetische velden. Deze manier van beelden maken wordt magnetisch resonantie (MR) beeldvorming genoemd en geeft een driedimensionaal beeld van het hart, samengesteld uit verschillende evenwijdige sneden. Deze beelden stellen de arts in staat het hart te bekijken en om bv. het volume van de hartkamers te meten. Deze metingen gebeuren momenteel manueel wat lang duurt (en dus duur is) omdat de arts op ongeveer 40 beelden de contouren van deze hartkamers moet aanduiden.

Het doel van dit thesisproject was dan ook dit meetproces te automatiseren en de computer deze metingen te laten uitvoeren. Een computer aanleren om de randen van de hartwand te detecteren is echter alles behalve eenvoudig. Hoewel onze hersenen bijvoorbeeld onmiddellijk beseffen dat een rood gebied in een foto eigenlijk een huis vormt, blijft het automatisch herkennen van objecten door een computer erg moeilijk. We vertellen aan een computer hoe hij dit toch kan realiseren door een algoritme te ontwikkelen dat verschillende stappen doorloopt:

1) Eerst zoekt de computer de positie van het hart in 1 snede. Dit is niet vanzelfsprekend omdat in deze snede ook andere organen en weefsels te zien zijn (zoals de longen of het middenrif) die gelijkaardige patronen in het beeld kunnen opleveren. Omdat we echter weten dat de linker hartkamer er in deze sneden ongeveer als een cirkel uitziet zoeken we eerst naar alle objecten die op een cirkel lijken. Hiervoor definiëren we op voorhand een aantal mogelijke dikwandige elliptische vormen (m.a.w. een verzameling van ellipsen met verschillende grootte, oriëntatie, excentriciteit en wanddikte) en proberen deze ergens in het beeld terug te vinden. Dit proces wordt ‘template matching’ genoemd. Eenmaal we ergens in het beeld een goed passende vorm gevonden hebben weten we ongeveer waar het hart zich in dat beeld bevindt (Figuur a).

2) De best passende vorm laten we vervolgens vervormen door de randen te verschuiven in de radiale richting, m.a.w. in een richting die door het middelpunt van de best passende ellips gaat. Hierbij wordt geprobeerd om de beeldwaarden voor het gebied binnen en buiten de contour zo homogeen mogelijk te maken. Met andere woorden, indien de best passende vorm oorspronkelijk een aantal witte pixels tot het hartwand zou rekenen, dan probeert het algoritme de rand te verschuiven zodat deze witte pixels niet meer tot de hartwand maar eerder tot de caviteit waar het bloed zich bevindt behoren. Aan de andere kant legt het algoritme de voorwaarde op dat de gevonden hartwand relatief glad is en geen scherpe uitsteeksels mag vertonen (want we weten dat de hartwand dergelijke uitsteeksels niet heeft). Op die manier vindt de computer uiteindelijk een compromis tussen de beeldinformatie en de ‘gladheid’ van de contour. Dit proces wordt ‘active contours’ genoemd, m.a.w. contouren die actief verschuiven gebruik makend van de beeldinformatie en eventuele extra voorwaarden voor de karakteristieken van de gewenste contour. Het resultaat van deze stap is dat de exacte positie van de hartwand binnen deze snede gekend is (Figuur b).

3) De informatie uit bovenstaande stap wordt overgedragen naar naburige sneden en we passen telkens opnieuw deze ‘active contours’ toe om de vorm aan te passen in elke snede. Hierbij houden we er rekening mee dat het hart een 3D dimensionale structuur is. Dit betekent dat het verschuiven van de rand in één snede invloed heeft op de positie van de rand in naburige sneden aangezien ook voor de derde dimensie een zeker ‘gladheid’ van het oppervlak vereist is. Op deze manier kennen we dus de positie van de hartwand in alle sneden en dus de volledige vorm in 3D (Figuur c).

4) Aangezien de hartwand beweegt in het beeld tijdens de pompende beweging van het hart leren we de computer ook nog hoe hij de beweging van de hartwand kan volgen. Hierbij wordt er vanuit gegaan dat de beeldwaarden niet veranderen wanneer het hart beweegt en dat het volgen van de gekende intensiteiten (o.b.v. stap 3) dan ook resulteert in het volgen van de hartwand. Zo kennen we niet alleen de 3D vorm van het hart maar ook de manier waarop deze vorm in de tijd verandert (Figuur d).

Het resultaat van bovenstaande stappen is dat we het volume van de hartkamer in elke fase van de hartcyclus kunnen meten en dus kunnen berekenen hoeveel bloed het hart wegpompt tijdens elke contractie. Dit is het zogenaamde slagvolume en geeft waardevolle informatie over de werking van het hart.

Het ontwikkelde algoritme werd aanvankelijk getest op 45 MR onderzoeken die ter beschikking werden gesteld door een vakvereniging en die voordien reeds door andere onderzoekers gebruikt werden om hun algoritmes te testen. Hiertoe werden deze beelden eerst manueel geanalyseerd en vervolgens door de computer. Het voorgestelde algoritme bleek niet alleen nauwkeuriger te zijn dan reeds bestaande algoritmes maar was bovendien ook veel sneller.

Vervolgens werd het algoritme getest op 318 MR onderzoeken opgenomen in de context van een grote klinische studie waarbij verschillende centra in Europa beeldmateriaal verzamelden en waarbij 1 expert centrum de beelden manueel had geanalyseerd. In 95% van de gevallen kon het voorgestelde algoritme binnen de 10 seconden - volautomatisch - nauwkeurige metingen genereren.

Dit thesiswerk heeft er dus toe geleid dat bij het meten van de vorm en functie van het hart de computer een deel van het werk van de artsen kan overnemen waardoor het onderzoek niet alleen veel sneller maar ook beter reproduceerbaar kan gebeuren. Het is dus een belangrijke stap voorwaarts in het verbeteren van de diagnose en behandeling van hartaandoeningen.

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