Deep Learning Assisted Localization of Polycystic Kidney on Contrast-Enhanced CT Images

Abstract

Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the kidneys by defining and delineating the kidney’s boundary slice by slice. However, kidney localization is a time-consuming and challenging task considering the unstructured medical images from big data such as Contrast-enhanced Computed Tomography (CCT). This study aimed to design an automatic localization model of ADPKD using Artificial Intelligence. A robust detection model using CCT images, image preprocessing, and Single Shot Detector (SSD) Inception V2 Deep Learning (DL) model is designed here. The model is trained and evaluated with 110 CCT images that comprise 10,078 slices. The experimental results showed that our derived detection model outperformed other DL detectors in terms of Average Precision (AP) and mean Average Precision (mAP). We achieved mAP = 94% for image-wise testing and mAP = 82% for subject-wise testing, when threshold on Intersection over Union (IoU) = 0.5. This study proves that our derived automatic detection model can assist radiologist in locating and classifying the ADPKD kidneys precisely and rapidly in order to improve the segmentation task and TKV calculation.

Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

“…This is especially important for MRI where minor improvements in the imaging technology are periodically implemented and need to be incorporated into the model. Unlike prior reports attempting to fully automate organ volume measurements into ADPKD with accuracy approaching manual contouring [22][23][24][25][26][27][28][29][30][31][32][33][34][35], this research demonstrates superior measurement reproducibility over manual contouring that can readily adapt to technological advances. Since the deep learning server is within the PACS firewall, technologists can rapidly transfer images to the server for running the inference.…”

“…This eliminates the need to manually draw every contour of the cystic kidneys, [ 22 ] thereby increasing the efficiency of accurate TKV measurement. Table 1 summarizes the existing literature for deep learning-based organ volume measurements in ADPKD using CT [ 23 , 24 , 25 , 26 , 27 ], ultrasound [ 28 ] and MRI [ 22 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. MRI has the advantage over CT of not requiring ionizing radiation, which is particularly important, for these organ volume measurements are repeated many times over the patient’s lifetime, and MRI has higher resolution compared to ultrasound.…”

Identifying Periampullary Regions in MRI Images Using Deep Learning

“…In this work, we developed deep learning structures to automatically segment the PA region using MRI T1 and T2 images. Recently, there were abundant reported studies developing AI algorithms for segmentation of abdominal organs or structures including pancreas (16), liver (17,18), spleen (35,36), gallbladder (37), kidney (38,39), the local lesions of stomach (40), etc. However, there is no report of PA region segmentation using AI algorithms.…”

Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

“…When considering the analysis for different cancers and diseases such as kidney disease [30], brain tumor, prostate cancer, and colon cancer [31], researchers have carried out several studies for automatic and semi-automatic analysis using AI [32]. In particular, considering the histology images analysis, there have been several research works conducted for not only colorectal cancer, but also different types of cancers [33], such as breast cancer, skin cancer [34], and renal cancer [35].…”