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Quantification of kidney function in Dynamic Contrast-Enhanced MagneticResonance Imaging (DCE-MRI) requires careful segmentation of the renal regionof interest (ROI). Traditionally, human experts are required to manuallydelineate the kidney ROI across multiple images in the dynamic sequence. Thisapproach is costly, time-consuming and labour intensive, and therefore acts tolimit patient throughout and acts as one of the factors limiting the wideradoption of DCR-MRI in clinical practice. Therefore, to address this issue, wepresent the first use of Dynamic Mode Decomposition (DMD) as a basis forautomatic segmentation of a dynamic sequence, in this case, kidney ROIs inDCE-MRI. Using DMD coupled combined with thresholding and connected componentanalysis is first validated on synthetically generated data with knownground-truth, and then applied to ten healthy volunteers' DCE-MRI datasets. Wefind that the segmentation result obtained from our proposed DMD framework iscomparable to that of expert observers and very significantly better than thatof an a-priori bounding box segmentation. Our result gives a mean Jaccardcoefficient of 0.87, compared to mean scores of 0.85, 0.88 and 0.87 producedfrom three independent manual annotations. This represents the first use of DMDas a robust automatic data-driven segmentation approach without requiring anyhuman intervention. This is a viable, efficient alternative approach to currentmanual methods of isolation of kidney function in DCE-MRI.

Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images