A semi-automated "blanket" method for renal segmentation from non-contrast T1-weighted MR images

Abstract

Objective: To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI.

Materials and methods: The user draws approximate kidney contours on every tenth slice, focusing on separating adjacent organs from the kidney. The program then performs a sequence of fully automatic steps: contour filling, interpolation, non-uniformity correction, sampling of representative parenchyma signal, and 3D binary morphology. Three independent observers applied the method to images of 40 kidneys ranging in volume from 94.6 to 254.5 cm(3). Manually constructed reference masks were used to assess accuracy.

Results: The volume errors for the three readers were: 4.4% ± 3.0%, 2.9% ± 2.3%, and 3.1% ± 2.7%. The relative discrepancy across readers was 2.5% ± 2.1%. The interactive processing time on average was 1.5 min per kidney.

Conclusions: Pending further validation, the semi-automated method could be applied for monitoring of renal status using non-contrast MRI.

Keywords: Kidney; MRI; Renal; Segmentation; Volume.

Fig. 1

A representative axial slice through the kidney before (top) and after (bottom) fat suppression. Note adjacent organs and skeletal muscle with MR signal similar to the kidney

Fig. 2

Observer selects for processing the inferior-most kidney slice (slice 2 in this example) and processes every tenth slice (2, 12, 22, 32, 42, and 52), always ending at the superior-most kidney slice. The goal is to separate the kidney from adjacent organs with signal intensity similar to that of the kidney. Enlarged slice 42 is on the right

Fig. 3

Over-inclusive contours after interior filling and z-axis interpolation

Fig. 4

Renal seed in coronal, axial, and sagittal views

Fig. 5

Final renal mask

Fig. 6

A representative kidney (upper row) with seed (lower row) drawn for Robust Statistics Segmenter. The seed consists of 3-mm-wide paintbrush strokes drawn in axial, sagittal, and coronal views

Fig. 7

Error in kidney volume as a function of two key parameters (plotted on the x and y axes) of the blanket method. See text for the definition of high threshold and erosion parameters. Left panel fat-suppressed images; right panel original T1-weighted data

Fig. 8

The effect of slice skip factor s on segmentation error (red dots, left axis) and on observer effort (green dots, right axis). The effort is expressed as the average number of slices traced. This number, multiplied by 10–15 s needed to trace one contour, yields the processing time per kidney

Fig. 9

Volume measured by three readers plotted against the true volume. Also shown are the regression line (solid) and the identity line (dashed)

Fig. 10

Examples of over- and under-segmentation errors for a representative right and left kidney. The top row shows the original images in coronal and axial views. The segmentation masks are shown on the bottom. Red: blanket method; yellow: reference mask; orange: overlap of the two masks. Note over-inclusion of hilar structures in the blanket method

Fig. 11

Bland–Altman plots of the differences between volumes estimated by pairs of readers. The solid lines show the mean difference between two readers (the bias), and the two dotted lines indicate 95 % limits of agreement (±2 standard deviations around the mean)