Transfer learning-based approach for automated kidney segmentation on multiparametric MRI sequences

Abstract

Purpose: Multiparametric magnetic resonance imaging (mp-MRI) is being investigated for kidney cancer because of better soft tissue contrast ability. The necessity of manual labels makes the development of supervised kidney segmentation algorithms challenging for each mp-MRI protocol. Here, we developed a transfer learning-based approach to improve kidney segmentation on a small dataset of five other mp-MRI sequences. Approach: We proposed a fully automated two-dimensional (2D) attention U-Net model for kidney segmentation on T1 weighted-nephrographic phase contrast enhanced (CE)-MRI (T1W-NG) dataset ( $N=108$ ). The pretrained weights of T1W-NG kidney segmentation model transferred to five other distinct mp-MRI sequences model (T2W, T1W-in-phase (T1W-IP), T1W-out-of-phase (T1W-OP), T1W precontrast (T1W-PRE), and T1W-corticomedullary-CE (T1W-CM), $N=50$ ) and fine-tuned by unfreezing the layers. The individual model performances were evaluated with and without transfer-learning fivefold cross-validation on average Dice similarity coefficient (DSC), absolute volume difference, Hausdorff distance (HD), and center-of-mass distance (CD) between algorithm generated and manually segmented kidneys. Results: The developed 2D attention U-Net model for T1W-NG produced kidney segmentation DSC of $89.34\pm 5.31\%$ . Compared with randomly initialized weight models, the transfer learning-based models of five mp-MRI sequences showed average increase of 2.96% in DSC of kidney segmentation ( $p=0.001$ to 0.006). Specifically, the transfer-learning approach increased average DSC on T2W from 87.19% to 89.90%, T1W-IP from 83.64% to 85.42%, T1W-OP from 79.35% to 83.66%, T1W-PRE from 82.05% to 85.94%, and T1W-CM from 85.65% to 87.64%. Conclusions: We demonstrate that a pretrained model for automated kidney segmentation of one mp-MRI sequence improved automated kidney segmentation on five other additional sequences.

Keywords: CNN models; kidney segmentation; multiparametric MRI; transfer learning.

Fig. 1

Schematic of attention U-Net model designed for kidney segmentation on T1W-NG images.

Fig. 2

Sample kidney segmentation using attention U-Net model on T1W-NG axial test slices from different patients, where yellow contours are ground truths and red contours are model predicted kidney segmentations.

Fig. 3

Sample kidney segmentation on T1W-NG test images where each column is representing a different patient with yellow contours are ground truths and red contours are model predicted outputs of kidney segmentations. The first column shows one of the apex region slices where kidney becomes visible. The second and third columns represent kidney segmentation on middle slices and last column is one of the bottom region kidney slices.

Fig. 4

Sample three cases showing 3D view of kidney segmentation on T1W-NG test images where original label (manual kidney segmentation) is compared with U-Net, cascade U-Net, U-Net++, dilated U-Net, residual U-Net, SegNet, ResNet50, and proposed attention U-Net model predicted kidney segmentation.

Fig. 5

Sample 2D coronal and sagittal views of kidney segmentation on T1W-NG test images on attention U-Net model where manual segmentation is in red, model prediction is in cyan, and overlap between manual segmentation and model predictions in yellow.

Fig. 6

Sample axial and 3D kidney segmentation view on target mp-MRI sequence images using without and with transfer learning approaches; where in axial slices yellow contours are ground truth kidney labels and red contours are model predictions.

Fig. 7

Sample 2D coronal and sagittal views of kidney segmentation on five different mp-MRI sequences test images using without and with transfer learning approaches where manual segmentation is in red, model prediction is in cyan, and overlap between manual segmentation and model predictions is in yellow.

Fig. 8

Sample kidney segmentation on axial slices is shown using combined dataset model predictions in column 2, without transfer learning approach model predictions in column 3, and with transfer learning approach model in column 4. Each row represents a separate test case where yellow contours are ground truth kidney labels and red contours are model predictions.