Abstract
Many renal cancers are incidentally found on non-contrast CT (NCCT) images. On contrast-enhanced CT (CECT) images, most kidney tumors, especially renal cancers, have different intensity values compared to normal tissues. However, on NCCT images, some tumors called isodensity tumors, have similar intensity values to the surrounding normal tissues, and can only be detected through a change in organ shape. Several deep learning methods which segment kidney tumors from CECT images have been proposed and showed promising results. However, these methods fail to capture such changes in organ shape on NCCT images. In this paper, we present a novel framework, which can explicitly capture protruded regions in kidneys to enable a better segmentation of kidney tumors. We created a synthetic mask dataset that simulates a protuberance, and trained a segmentation network to separate the protruded regions from the normal kidney regions. To achieve the segmentation of whole tumors, our framework consists of three networks. The first network is a conventional semantic segmentation network which extracts a kidney region mask and an initial tumor region mask. The second network, which we name protuberance detection network, identifies the protruded regions from the kidney region mask. Given the initial tumor region mask and the protruded region mask, the last network fuses them and predicts the final kidney tumor mask accurately. The proposed method was evaluated on a publicly available KiTS19 dataset, which contains 108 NCCT images, and showed that our method achieved a higher dice score of 0.615 (+0.097) and sensitivity of 0.721 (+0.103) compared to 3D-UNet. To the best of our knowledge, this is the first deep learning method that is specifically designed for kidney tumor segmentation on NCCT images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Estimated number of new cases in 2020, world, both sexes, all ages (excl. nmsc). https://gco.iarc.fr/today/online-analysis-table/. Accessed 27 Feb 2023
Bradbury, J., et al.: JAX: composable transformations of Python+NumPy programs (2018). http://github.com/google/jax
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: Proceedings of International Conference on Learning Representations (2019)
Golts, A., Khapun, D., Shats, D., Shoshan, Y., Gilboa-Solomon, F.: An ensemble of 3D U-net based models for segmentation of kidney and masses in CT scans. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds.) KiTS 2021. LNCS, vol. 13168, pp. 103–115. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98385-7_14
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)
Heller, N., et al.: C4KC kits challenge kidney tumor segmentation dataset (2019)
Hennigan, T., Cai, T., Norman, T., Babuschkin, I.: Haiku: Sonnet for JAX (2020). http://github.com/deepmind/dm-haiku
Hou, X., Chunmei, X., Li, F., Yang, N.: Cascaded semantic segmentation for kidney and tumor. Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19 (2019)
Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2020)
Isensee, F., Maier-Hein, K.: An attempt at beating the 3D U-NET. Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19 (2019)
Liu, J., Wang, S., Linguraru, M.G., Yao, J., Summers, R.M.: Computer-aided detection of exophytic renal lesions on non-contrast CT images. Med. Image Anal. 19(1), 15–29 (2015)
Liu, J., Wang, S., Yao, J., Linguraru, M.G., Summers, R.M.: Manifold diffusion for exophytic kidney lesion detection on non-contrast CT images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 340–347. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_43
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Mu, G., Lin, Z., Han, M., Yao, G., Gao, Y.: Segmentation of kidney tumor by multi-resolution VB-Nets. Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19 (2019)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Pinsky, P.F., et al.: Incidental renal tumours on low-dose CT lung cancer screening exams. J. Med. Screen. 24(2), 104–109 (2017)
Touijer, K., et al.: The expanding role of partial nephrectomy: a critical analysis of indications, results, and complications. Eur. Urol. 57(2), 214–222 (2010)
Zhao, Z., Chen, H., Wang, L.: A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds.) KiTS 2021. LNCS, vol. 13168, pp. 53–58. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98385-7_8
Znaor, A., Lortet-Tieulent, J., Laversanne, M., Jemal, A., Bray, F.: International variations and trends in renal cell carcinoma incidence and mortality. Eur. Urol. 67(3), 519–530 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hatsutani, T., Ichinose, A., Nakamura, K., Kitamura, Y. (2023). Segmentation of Kidney Tumors on Non-Contrast CT Images Using Protuberance Detection Network. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-43990-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43989-6
Online ISBN: 978-3-031-43990-2
eBook Packages: Computer ScienceComputer Science (R0)
