Boundary-Aware Network for Kidney Parsing

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65$\%$ for kidney structure segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net.

[1]  Yang Chen,et al.  MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation , 2022, IJCAI.

[2]  Ramalingam Chellappa,et al.  HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Lixu Gu,et al.  AdwU-Net: Adaptive Depth and Width U-Net for Medical Image Segmentation by Differentiable Neural Architecture Search , 2022, MIDL.

[4]  Youyong Kong,et al.  Meta grayscale adaptive network for 3D integrated renal structures segmentation , 2021, Medical Image Anal..

[5]  Josien P. W. Pluim,et al.  clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Yaozong Gao,et al.  The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge , 2019, Medical Image Anal..

[7]  Jens Petersen,et al.  nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.

[8]  Jiasong Wu,et al.  Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation , 2020, Medical Image Anal..

[9]  Septimiu E. Salcudean,et al.  Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[10]  Yong Xia,et al.  Boundary-Aware Network for Kidney Tumor Segmentation , 2020, MLMI@MICCAI.

[11]  Weidong Cai,et al.  HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images , 2019, MICCAI.

[12]  Jose Dolz,et al.  Boundary loss for highly unbalanced segmentation , 2018, MIDL.

[13]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[14]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[15]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[16]  P. Shao,et al.  Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy. , 2012, European urology.

[17]  Chao Qin,et al.  Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes. , 2011, European urology.