Kidney Segmentation Research Articles

Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney's shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels' fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.

Renal artery-based kidney segmentation on CT for patients with renal cell carcinoma: Feasibility of segmental artery clamping simulation

PurposeTo evaluate the feasibility of renal artery-based segmentation of kidneys with renal cell carcinoma (RCC), based on three-dimensional (3D) software for the simulation of segmental artery clamping (SAC), and to correlate it with RENAL nephrometry score. MethodsFifty RCCs (< 4 cm) identified from a pathological database search between January 2015 and January 2018 were included retrospectively. On computed tomography (CT) images, the relevant kidney, tumor, and renal artery were annotated semi-automatically on the commercial workstation, and renal artery-based segmentation was performed using 3D Voronoi diagrams. Simulation of SAC was performed by a radiologist and urologist in consensus. The volume of the whole kidney and tumor and estimated rescued volume for possible SAC cases were calculated. The correlation between possible SAC and RENAL nephrometry score was investigated. The reproducibility of the calculation of each volume and the interrater reliability of SAC simulation were assessed. ResultsIn the anatomical analysis, 44 patients had a single main renal artery and six had two main renal arteries, and of these, an early division pattern was observed in 11 cases. In the 3D simulation software, 22 out of 50 cases (44 %) were determined as possible SAC. The agreement of the SAC simulation was excellent (kappa = 0.96). RENAL nephrometry score was significantly different in the anterior/posterior and exophytic/endophytic components between possible and impossible SAC groups. ConclusionsRenal artery-based segmentation of kidneys with RCC on CT images using 3D simulation software is feasible for effectively estimating the possibility of SAC with high reproducibility.

Multi-scale random walk driven adaptive graph neural network with dual-head neighboring node attention for CT segmentation

Segmenting objects with indistinct boundaries and large variations from CT volumes is a challenging issue due to overlapping intensity distributions from neighboring tissues or long-distance semantic relations. We propose a multi-scale random walk (RW) driven graph neural network (GNN) to address this issue. A graph is first initialized to represent image regions and deep semantic features from the segmentation encoder by graph nodes and attributes. We then propose a multi-scale graph reasoning model where for each scale, graph node attribute embedding is obtained by an adaptive GNN with dual-head neighboring node attention, while graph topology is evolved by RW. The neighboring-node attention mechanism is designed to learn and incorporate the importance and influence of neighboring nodes on their connected nodes. Random walking to multi-order neighbors enhance the contextual information formulation and diffusion along graph edges. Finally, multi-scale knowledge learnt from graphs is adaptively fused by a new graph-wise attention fusion module before reshaping and feeding to the segmentation decoder. We evaluate the contributions of major innovations by ablation studies, comparison with other state-of-the-art models on public kidney and tumor segmentation dataset. The generalization ability of our model is validated by different segmentation backbones. Experimental results show that the novel multi-scale adaptive graph reasoning architecture and RW-enhanced GNN model improved the segmentation of objects from adjacent tissues.

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation

Objective. Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks. Approach. We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region node by performing multi-order random walks (RW) on the graph and updating neighboring topologies at different neighborhood ranges. Afterwards, multi-scale graph convolutional autoencoders are developed to extract deep multi-scale topological representations of nodes and propagate learnt knowledge along graph edges during the convolutional and optimization process. We also propose a scale-level attention module to learn the adaptive weights of topological representations at multiple scales for enhanced fusion. Finally, the enhanced topological representation and knowledge from graph reasoning are integrated with content features before feeding into the segmentation decoder. Main results. The evaluation results over public kidney and tumor CT segmentation dataset show that our model outperforms other state-of-the-art segmentation methods. Ablation studies and experiments using different convolutional neural networks backbones show the contributions of major technical innovations and generalization ability. Significance. We propose for the first time an RW-driven MCG with scale-level attention to extract semantic connections and spatial dependencies between a diverse range of regions for accurate kidney and tumor segmentation in CT volumes.

Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics.

The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney is a fundamental step in the early and noninvasive detection of acute renal allograft rejection. In this paper, a new and accurate DCE‑MRI kidney segmentation method is proposed. In this method, fuzzy c-means (FCM) clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution. Moreover, population‑based shape (PB-shape) and subject-specific shape (SS-shape) statistics are both exploited. The PB-shape model is trained offline from ground-truth kidney segmentations of various subjects, whereas the SS-shape model is trained on the fly using the segmentation results that are obtained for a specific subject. The proposed method was evaluated on the real medical datasets of 45 subjects and reports a Dice similarity coefficient (DSC) of 0.953 ± 0.018, an intersection-over-union (IoU) of 0.91 ± 0.033, and 1.10 ± 1.4 in the 95-percentile of Hausdorff distance (HD95). Extensive experiments confirm the superiority of the proposed method over several state-of-the-art level set methods, with an average improvement of 0.7 in terms of HD95. It also offers an HD95 improvement of 9.5 and 3.8 over two deep neural networks based on the U-Net architecture. The accuracy improvements have been experimentally found to be more prominent on low-contrast and noisy images.

Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney’s shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels.

Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT.

Knowledge of kidney stone composition can help in patient management; urine composition analysis and dual-energy CT are frequently used to assess stone type. We assessed if threshold-based stone segmentation and radiomics can determine the composition of kidney stones from single-energy, non-contrast abdomen-pelvis CT. With IRB approval, we identified 218 consecutive patients (mean age 64 ± 13years; male:female 138:80) with the presence of kidney stones on non-contrast, abdomen-pelvis CT and surgical or biochemical proof of their stone composition. CT examinations were performed on one of the seven multidetector-row scanners from four vendors (GE, Philips, Siemens, Toshiba). Deidentified CT images were processed with a radiomics prototype (Frontier, Siemens Healthineers) to segment the entire kidney volumes with an AI-based organ segmentation tool. We applied a threshold of 130HU to isolate stones in the segmented kidneys and to estimate radiomics over the segmented stone volume. A coinvestigator verified kidney stone segmentation and adjusted the volume of interest to include the entire stone volume when necessary. We applied multiple logistic regression tests with precision recall plots to obtain area under the curve (AUC) using a built-in R statistical program. The threshold-based stone segmentation successfully isolated kidney stones (uric acid: n = 102 patients, calcium oxalate/phosphate: n = 116 patients) in all patients. Radiomics differentiated between calcium and uric acid stones with an AUC of 0.78 (p < 0.01, 95% CI 0.73-0.83), 0.79 sensitivity, and 0.90 specificity regardless of CT vendors (GE CT: AUC = 0.82, p < 0.01, 95% CI 0.740-0896; Siemens CT: AUC = 0.77, 95% CI 0.700-0.846, p < 0.01). Automated threshold-based stone segmentation and radiomics can differentiate between calcium oxalate/phosphate and urate stones from non-contrast, single-energy abdomen CT.

Fully Automated Segmentation of Kidneys and Exophytic Cysts Using Deep Convolutional Neural Networks in Subjects With Autosomal Dominant Polycystic Kidney Disease

Fully Automated Segmentation of Kidneys and Exophytic Cysts Using Deep Convolutional Neural Networks in Subjects With Autosomal Dominant Polycystic Kidney Disease

Segmentation of kidney mass using AgDenseU-Net 2.5D model

The Kidney and Kidney Tumor Segmentation Challenge 2021 (KiTS21) released a kidney CT dataset with 300 patients. Unlike KiTS19, KiTS21 provided a cyst category. Therefore, the segmentation of kidneys, tumors, and cysts will be able to assess the complexity and aggressiveness of kidney mass. Deep learning models can save medical resources, but 3D models still have some disadvantages, such as the high cost of computing resources. This paper proposes a scheme that saves computing resources and achieves the segmentation of kidney mass in two steps. First, we preprocess the kidney volume data using the automatic down-sampling method of 3D images, reducing the volume while preserving the feature information. Second, we finely segment kidneys, tumors, and cysts using the AgDenseU-Net (Attention gate DenseU-Net) 2.5D model. KiTS21 proposed using Hierarchical Evaluation Classes (HECs) to compute a metric for the superset: the HEC of kidney considers kidneys, tumors, and cysts as the foreground to compute segmentation performance; the HEC of kidney mass considers both tumor and cyst as the foreground classes; the HEC of tumor considers tumor as the foreground only. For KiTS21, our model achieved a dice score of 0.971 for the kidney, 0.883 for the mass, and 0.815 for the tumor. In addition, we also tested segmentation results without HECs, and our model achieved a dice score of 0.950 for the kidney, 0.878 for the tumor, and 0.746 for the cyst. The results demonstrate that the method proposed in this paper can be used as a reference for kidney tumor segmentation.

FYU-Net: A Cascading Segmentation Network for Kidney Tumor Medical Imaging.

Automated segmentation of renal tumors is essential for the diagnostic evaluation of kidney cancer. However, renal tumor volume is generally small compared with the volume of the kidney and is irregularly distributed; moreover, the location and shape of renal tumors are highly variable, making the segmentation task extremely challenging. To solve the aforementioned problems, a cascaded segmentation model (FYU-Net) for computed tomography (CT) images is proposed in this paper to achieve automatic kidney tumor segmentation. The proposed model involves two main steps. In the first step, a fast scan of the kidney CT data is performed using a localization network to find slices containing tumors, and coarse segmentation is performed simultaneously. In the second step, a segmentation framework embedded with the feature pyramid network module is employed to finely segment kidney tumors. By building a feature pyramid structure, targets of different sizes are distributed to be detected on different feature layers to extract richer feature information. In addition, the top-down structure allows the information of the higher-level feature maps to be transferred to the lower-level feature maps, enhancing the semantic information of the lower-level feature maps. Comparative experiments were conducted on the Kidney PArsing Challenge 2022 public dataset; the average Jaccard coefficient and average Dice coefficient of tumor structure segmentation were more than 70.73% and more than 82.85%, respectively. The results demonstrate the effectiveness of the proposed model for kidney tumor segmentation.

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint.

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the same organ. This is due to the significant intensity variations of different image modalities. In this paper, we propose a novel end-to-end deep neural network to achieve multi-modality image segmentation, where image labels of only one modality (source domain) are available for model training and the image labels for the other modality (target domain) are not available. In our method, a multi-resolution locally normalized gradient magnitude approach is firstly applied to images of both domains for minimizing the intensity discrepancy. Subsequently, a dual task encoder-decoder network including image segmentation and reconstruction is utilized to effectively adapt a segmentation network to the unlabeled target domain. Additionally, a shape constraint is imposed by leveraging adversarial learning. Finally, images from the target domain are segmented, as the network learns a consistent latent feature representation with shape awareness from both domains. We implement both 2D and 3D versions of our method, in which we evaluate CT and MRI images for kidney and cardiac tissue segmentation. For kidney, a public CT dataset (KiTS19, MICCAI 2019) and a local MRI dataset were utilized. The cardiac dataset was from the Multi-Modality Whole Heart Segmentation (MMWHS) challenge 2017. Experimental results reveal that our proposed method achieves significantly higher performance with a much lower model complexity in comparison with other state-of-the-art methods. More importantly, our method is also capable of producing superior segmentation results than other methods for images of an unseen target domain without model retraining. The code is available at GitHub (https://github.com/MinaJf/LMISA) to encourage method comparison and further research.

Применение уретероуретеростомии при удвоении почки. Обзор литературы.

Introduction. Kidney duplication is one of the most common urological developmental anomalies, the incidence of which is 0.8%. At the same time, various types of obstructive uropathy can be a concomitant pathology in such a situation. There are various methods for correcting obstructive uropathy. Currently, more and more often in the treatment of obstructive uropathy of one of the segments of the doubled kidney, a method such as ureteroureterostomy is used. However, there is still no consensus on which treatment tactics in this case is the most optimal. The aim of our work was to analyze literature about using ureteroureterostomy in patients with obstructive uropathies of a duplicated kidney, as well as to compare this technique with others. Materials and methods. Search results were analyzed in the scientific databases e-Library, PubMed, Medline, Web of Science, Embase, Cochrane Library and PEDro according to the following. keywords: «renal duplication», «heminephrectomy», «ureteroureterostomy», «obstructive uropathies». In total it was found 157 publications, after analyzing the literature, 33 papers were selected for review, most fully reflecting the topic. Results. Heminephrectomy is still popular for severely reduced or absent duplex kidney function, but it has complications such as decreased function of the remaining segment, pseudocyst formation, stump, urinoma, segmental hematoma, ureteral stricture, and vesicoureteral reflux into the remaining segment. Ureteroureterostomy, as an alternative method for correcting obstructive uropathies of a double kidney, has been recognized as effective and safe in many studies. Data are presented to prove that ureteroureterostomy is applicable for any function of the affected segment. In this article, various techniques for ureteroureterostomy are consecrated, from open intervention to robotic one. In addition, research data on the safety of ureteroureteroanastomosis in case of a pronounced discrepancy between the diameters of the ureters are presented. Among the complications of ureteroureteroanastomosis, cultitis, urine leakage in the anastomosis zone, urinoma, and stenosis of the anastomosis can be noted. The predictors of an unfavorable outcome of the proposed method were identified. These include ectopic ureterocele, hydronephrosis of both segments of the duplex kidney, as well as simultaneous ureteroureterostomy and reimplantation of the recipient ureter. Conclusions. Ureteroureterostomy in patients with various pathologies inherent in duplication of the kidney is an effective and safe technique that allows preserving the function of the segment and adequately draining it. The use of this technique does not depend on the presence of a decrease or lack of function of one of the segments.

Improvement of renal image recognition through resolution enhancement

Image resizing is frequently used as a preprocessing step in many computer vision tasks, especially in medical applications. While tuning of the resizing method is usually omitted in the studies, there are many problems in which the exact influence of resampling on image textures and gradients is significant. The paper presents an in-depth analysis of image reconstruction’s impact on two inherent tasks in medical image analysis: segmentation and classification. The proposed study is conducted on the renal diagnosis dataset in which the kidney is segmented, and three renal tumours are classified. A novel image reconstruction method is introduced, namely Sampling Kantorovich Algorithm (SKA). It is compared to six other popular techniques widely used in image processing. Based on the qualitative and quantitative analyses, we proved that choice of image reconstruction method impacts the system’s overall performance. SKA turns out to be the best performing method in the classification setup. It boosts performance to 75% of the weighted F1-score by approximately 3 percentage points (pp) compared to the best baseline solution. In kidney segmentation, the SKA improves efficiency by over 2pp compared to other resizing methods. The results presented in this paper may apply to a wide range of medical image processing problems.

Automatic pancreas segmentation using ResNet-18 deep learning approach

The accurate pancreas segmentation process is essential in the early detection of pancreatic cancer. The pancreas is situated in the abdominal cavity of the human body. The abdominal cavity contains the pancreas, liver, spleen, kidney, and adrenal glands. Sharp and smooth detection of the pancreas from this abdominal cavity is a challenging and tedious job in medical image investigation. Top-down approaches like Novel Modified K-means Fuzzy clustering algorithm (NMKFCM), Scale Invariant Feature Transform (SIFT), Kernel Density Estimator (KDE) algorithms were applied for pancreas segmentation in the early days. Recently, Bottom-up method has become popular for pancreas segmentation in medical image analysis and cancer diagnosis. LevelSet algorithm is used to detect the pancreas from the abdominal cavity. The deep learning, bottom-up approach performance is better than another. Deep Residual Network (ResNet-18) deep learning, bottom-up approach is used to detect accurate and sharp pancreas from CT scan medical images. 18 layers are used in the architecture of ResNet-18. The automatic pancreas and kidney segmentation is accurately extracted from CT scan images. The proposed method is applied to the medical CT scan images dataset of 82 patients. 699 images and 150 images with different angles are used for training and testing purposes, respectively. ResNet-18 attains a dice similarity index value up to 98.29±0.63, Jaccard Index value up to 96.63±01.25, Bfscore value up to 84.65±03.96. The validation accuracy of the proposed method is 97.01%, and the loss rate value achieves up to 0.0010. The class imbalance problem is solved by class weight and data augmentation.

YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images

YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images

S2S-ARSNet: Sequence-to-Sequence automatic renal segmentation network

Accurate segmentation of kidney contours in diuretic renography has important implications for clinical diagnosis and treatments. However, the lack of clear boundaries and high-quality images makes automatic segmentation challenging. This paper proposes a novel automatic renal segmentation network, S2S-ARSNet, combining Convolutional Long Short-Term Memory (ConvLSTM) with the Unet structure. Unet is used to learn the spatial information of each sequence, and ConvLSTM is used to discover the temporal information between sequences and automatically update the temporal state of the sequence. Moreover, an additional pre-trained Unet is applied to generate coarse masks at different times to simulate the displacement that may occur during the detection process. In this way, the spatiotemporal information is modelled, and all the information of the 3D data is fully utilized to eliminate false positives and improve the segmentation accuracy. Extensive experiments were performed on the diuretic renography dataset. The experimental results show that the proposed method can significantly enhance the kidney segmentation performance compared with other single-image-based deep learning segmentation methods.

Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet.

The precise segmentation of the kidneys in computed tomography (CT) images is vital in urology for diagnosis, treatment, and surgical planning. Medical experts can get assistance through segmentation, as it provides information about kidney malformations in terms of shape and size. Manual segmentation is slow, tedious, and not reproducible. An automatic computer-aided system is a solution to this problem. This paper presents an automated kidney segmentation technique based on active contour and deep learning. In this work, 210 CTs from the KiTS 19 repository were used. The used dataset was divided into a train set (168 CTs), test set (21 CTs), and validation set (21 CTs). The suggested technique has broadly four phases: (1) extraction of kidney regions using active contours, (2) preprocessing, (3) kidney segmentation using 3D U-Net, and (4) reconstruction of the segmented CT images. The proposed segmentation method has received the Dice score of97.62%, Jaccard index of 95.74%, average sensitivity of 98.28%, specificity of 99.95%, and accuracy of 99.93% over the validation dataset. The proposed method can efficiently solve the problem of tumorous kidney segmentation in CT images by using active contour and deep learning. The active contour was used to select kidney regions and 3D-UNet was used for precisely segmenting the tumorous kidney.

A Deep Learning Approach for Automated Segmentation of Kidneys and Exophytic Cysts in Individuals with Autosomal Dominant Polycystic Kidney Disease.

Total kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming. We developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts. We used abdominal T2 -weighted magnetic resonance images from 210 individuals with ADPKD who were divided into two groups: one group of 157 to train the network and a second group of 53 to test it. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation, in that 80% of 157 cases were for training and the remaining 20% were for validation. We used Dice similarity coefficient, intraclass correlation coefficient, and Bland-Altman analysis to assess the performance of the automated segmentation method compared with the manual method. The automated and manual reference methods exhibited excellent geometric concordance (Dice similarity coefficient: mean±SD, 0.962±0.018) on the test datasets, with kidney volumes ranging from 178.9 to 2776.0 ml (mean±SD, 1058.5±706.8 ml) and exophytic cysts ranging from 113.4 to 2497.6 ml (mean±SD, 549.0±559.1 ml). The intraclass correlation coefficient was 0.9994 (95% confidence interval, 0.9991 to 0.9996; P<0.001) with a minimum bias of -2.424 ml (95% limits of agreement, -49.80 to 44.95). We developed a fully automated segmentation method to measure TKV that excludes exophytic cysts and has an accuracy similar to that of a human expert. This technique may be useful in clinical studies that require automated computation of TKV to evaluate progression of ADPKD and response to treatment.

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.

Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.