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Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet

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Abstract

Background and purpose

The precise segmentation of the kidneys in computed tomography (CT) images is vital in urology for diagnosis, treatment, and surgical planning. Medical experts can get assistance through segmentation, as it provides information about kidney malformations in terms of shape and size. Manual segmentation is slow, tedious, and not reproducible. An automatic computer-aided system is a solution to this problem. This paper presents an automated kidney segmentation technique based on active contour and deep learning.

Materials and methods

In this work, 210 CTs from the KiTS 19 repository were used. The used dataset was divided into a train set (168 CTs), test set (21 CTs), and validation set (21 CTs). The suggested technique has broadly four phases: (1) extraction of kidney regions using active contours, (2) preprocessing, (3) kidney segmentation using 3D U-Net, and (4) reconstruction of the segmented CT images.

Results

The proposed segmentation method has received the Dice score of 97.62%, Jaccard index of 95.74%, average sensitivity of 98.28%, specificity of 99.95%, and accuracy of 99.93% over the validation dataset.

Conclusion

The proposed method can efficiently solve the problem of tumorous kidney segmentation in CT images by using active contour and deep learning. The active contour was used to select kidney regions and 3D-UNet was used for precisely segmenting the tumorous kidney.

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  1. School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra-182320, Jammu & Kashmir, India

    Mohit Pandey & Abhishek Gupta

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  1. Mohit Pandey
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Pandey, M., Gupta, A. Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet. Ir J Med Sci 192, 1401–1409 (2023). https://doi.org/10.1007/s11845-022-03113-8

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