Oxford Medical Image Segmentation

Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense.

We have automated organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data.

We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts.

Quantitative analysis of our automated segmentation compared against hand–segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume–based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size–based measures (such as the relative volume difference).

The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen.

In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter–operator variability.

Whilst the procedure is fully automated, our tools also provide a light level of manual editing.