KEYWORDS: Image segmentation, Kidney, Magnetic resonance imaging, Education and training, Data modeling, 3D modeling, Signal intensity, Performance modeling, 3D image processing, Data acquisition
Segmentation of the renal parenchyma responsible for a renal function is necessary for surgical planning and decisionmaking of renal partial nephrectomy (RPN) by identifying the correlation between the renal parenchyma volume and renal function after RPN on abdominal magnetic resonance (MR) images without radiation exposure. This paper proposes a cascaded self-adaptive framework that uses local context-aware mix-up regularization on abdominal MR images acquired from multiple devices. The proposed renal parenchyma segmentation network consists of two stages: kidney bounding volume extraction and renal parenchyma segmentation. Before kidney bounding volume extraction, self-adaptive normalization is performed using nnU-Net as the backbone network to reduce differences in signal intensity and pixel spacing among MR images of different intensity ranges acquired from multiple MR devices. In the kidney bounding volume extraction stage, the renal parenchyma area is segmented using 3D U-Net with low-resolution data down-sampled twice from the original to efficiently localize the kidney in the abdomen. Bounding volume is generated to focus on the renal parenchyma area during the renal parenchyma segmentation stage by cropping to the volume-of-interest region using the segmentation results up-sampled to the original resolution. In the segmentation stage, the renal parenchyma is segmented using 3D U-Net with mix-up augmented bounding volume to improve the regularization performance of the model. The average F1-score of our method was 92.27%, which was 3.07%p and 0.32%p higher than the segmentation method using original 3D cascaded nnU-Net and 3D cascaded nnU-Net with kidney bounding volume extraction, respectively.
Segmentation of renal parenchyma responsible for renal function is necessary to evaluate contralateral renal hypertrophy and to predict renal function after renal partial nephrectomy (RPN). Although most studies have segmented the kidney on CT images to analyze renal function, renal function analysis is required without radiation exposure by segmenting the renal parenchyma on MR images. However, renal parenchyma segmentation is difficult due to small area in the abdomen, blurred boundary, large variations in the shape of kidney among patients, similar intensities with nearby organs such as the liver, spleen and vessels. Furthermore, signal intensity is different for each data due to a lot of movement when taking abdominal MR even when photographed with the same device. Therefore, we propose cascaded deep convolutional neural network for renal parenchyma segmentation with signal intensity correction in abdominal MR images. First, intensity normalization is performed in the whole MR image. Second, kidney is localized using 2D segmentation networks based on attention UNet on the axial, coronal, sagittal planes and combining through a majority voting. Third, signal intensity correction between each data is performed in the localized kidney area. Finally, renal parenchyma is segmented using 3D segmentation network based on UNet++. The average DSC of renal parenchyma was 91.57%. Our method can be used to assess contralateral renal hypertrophy and to predict renal function by measuring volume change of the renal parenchyma on MR images without radiation exposure instead of CT images, and can establish basis for treatment after RPN.
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