Computer-aided kidney segmentation on abdominal CT images

Kidney medicine meets computer vision: a bibliometric analysis

BACKGROUND AND OBJECTIVE

Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research.

METHODS

The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer.

RESULTS

There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities.

CONCLUSION

The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation

Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current deep learning-based methods for 3D multi-organ segmentation face challenges such as the need for labor-intensive manual pixel-level annotations and high hardware resource demands, especially regarding GPU resources. To address these issues, we propose a 3D proxy-bridged region-growing framework specifically designed for the segmentation of the liver and spleen. Specifically, a key slice is selected from each 3D volume according to the corresponding intensity histogram. Subsequently, a deep learning model is employed to pinpoint the semantic central patch on this key slice, to calculate the growing seed. To counteract the impact of noise, segmentation of the liver and spleen is conducted on superpixel images created through proxy-bridging strategy. The segmentation process is then extended to adjacent slices by applying the same methodology iteratively, culminating in the comprehensive segmentation results. Experimental results demonstrate that the proposed framework accomplishes segmentation of the liver and spleen with an average Dice Similarity Coefficient of approximately 0.93 and a Jaccard Similarity Coefficient of around 0.88. These outcomes substantiate the framework's capability to achieve performance on par with that of deep learning methods, albeit requiring less guidance information and lower GPU resources.

Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: A data pair generative adversarial network approach

In recent years, deep learning (DL) has been recognized very useful in the semantic segmentation of biomedical images. Such an application, however, is significantly hindered by the lack of pixel-wise annotations. In this work, we propose a data pair generative adversarial network (DPGAN) for the purpose of synthesizing concurrently the diverse biomedical images and the segmentation labels from random latent vectors. First, a hierarchical structure is constructed consisting of three variational auto-encoder generative adversarial networks (VAEGANs) with an extra discriminator. Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we divide the DPGAN into three stages, namely, background stage, mask stage and advanced stage, with each stage deploying a VAEGAN. In such a way, a large number of new segmentation data pairs are generated from random latent vectors and then used to augment the original data sets. Finally, to validate the effectiveness of the proposed DPGAN, experiments are carried out on a vestibular schwannoma data set, a kidney tumor data set and a skin cancer data set. The results indicate that, in comparison to other state-of-the-art GAN-based methods, the proposed DPGAN shows better performance in the generative quality, and meanwhile, gains an effective boost on semantic segmentation of class imbalanced biomedical images.

Grayscale self-adjusting network with weak feature enhancement for 3D lumbar anatomy segmentation

The automatic segmentation of lumbar anatomy is a fundamental problem for the diagnosis and treatment of lumbar disease. The recent development of deep learning techniques has led to remarkable progress in this task, including the possible segmentation of nerve roots, intervertebral discs, and dural sac in a single step. Despite these advances, lumbar anatomy segmentation remains a challenging problem due to the weak contrast and noise of input images, as well as the variability of intensities and size in lumbar structures across different subjects. To overcome these challenges, we propose a coarse-to-fine deep neural network framework for lumbar anatomy segmentation, which obtains a more accurate segmentation using two strategies. First, a progressive refinement process is employed to correct low-confidence regions by enhancing the feature representation in these regions. Second, a grayscale self-adjusting network (GSA-Net) is proposed to optimize the distribution of intensities dynamically. Experiments on datasets comprised of 3D computed tomography (CT) and magnetic resonance (MR) images show the advantage of our method over current segmentation approaches and its potential for diagnosing and lumbar disease treatment.

A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images

This paper proposes an encoder-decoder architecture for kidney segmentation. A hyperparameter optimization process is implemented, including the development of a model architecture, selecting a windowing method and a loss function, and data augmentation. The model consists of EfficientNet-B5 as the encoder and a feature pyramid network as the decoder that yields the best performance with a Dice score of 0.969 on the 2019 Kidney and Kidney Tumor Segmentation Challenge dataset. The proposed model is tested with different voxel spacing, anatomical planes, and kidney and tumor volumes. Moreover, case studies are conducted to analyze segmentation outliers. Finally, five-fold cross-validation and the 3D-IRCAD-01 dataset are used to evaluate the developed model in terms of the following evaluation metrics: the Dice score, recall, precision, and the Intersection over Union score. A new development and application of artificial intelligence algorithms to solve image analysis and interpretation will be demonstrated in this paper. Overall, our experiment results show that the proposed kidney segmentation solutions in CT images can be significantly applied to clinical needs to assist surgeons in surgical planning. It enables the calculation of the total kidney volume for kidney function estimation in ADPKD and supports radiologists or doctors in disease diagnoses and disease progression.

Focal Dice Loss-Based V-Net for Liver Segments Classification

Liver segmentation is a crucial step in surgical planning from computed tomography scans. The possibility to obtain a precise delineation of the liver boundaries with the exploitation of automatic techniques can help the radiologists, reducing the annotation time and providing more objective and repeatable results. Subsequent phases typically involve liver vessels’ segmentation and liver segments’ classification. It is especially important to recognize different segments, since each has its own vascularization, and so, hepatic segmentectomies can be performed during surgery, avoiding the unnecessary removal of healthy liver parenchyma. In this work, we focused on the liver segments’ classification task. We exploited a 2.5D Convolutional Neural Network (CNN), namely V-Net, trained with the multi-class focal Dice loss. The idea of focal loss was originally thought as the cross-entropy loss function, aiming at focusing on “hard” samples, avoiding the gradient being overwhelmed by a large number of falsenegatives. In this paper, we introduce two novel focal Dice formulations, one based on the concept of individual voxel’s probability and another related to the Dice formulation for sets. By applying multi-class focal Dice loss to the aforementioned task, we were able to obtain respectable results, with an average Dice coefficient among classes of 82.91%. Moreover, the knowledge of anatomic segments’ configurations allowed the application of a set of rules during the post-processing phase, slightly improving the final segmentation results, obtaining an average Dice coefficient of 83.38%. The average accuracy was close to 99%. The best model turned out to be the one with the focal Dice formulation based on sets. We conducted the Wilcoxon signed-rank test to check if these results were statistically significant, confirming their relevance.

Kidney Tumor Segmentation Based on FR2PAttU-Net Model

The incidence rate of kidney tumors increases year by year, especially for some incidental small tumors. It is challenging for doctors to segment kidney tumors from kidney CT images. Therefore, this paper proposes a deep learning model based on FR2PAttU-Net to help doctors process many CT images quickly and efficiently and save medical resources. FR2PAttU-Net is not a new CNN structure but focuses on improving the segmentation effect of kidney tumors, even when the kidney tumors are not clear. Firstly, we use the R2Att network in the "U" structure of the original U-Net, add parallel convolution, and construct FR2PAttU-Net model, to increase the width of the model, improve the adaptability of the model to the features of different scales of the image, and avoid the failure of network deepening to learn valuable features. Then, we use the fuzzy set enhancement algorithm to enhance the input image and construct the FR2PAttU-Net model to make the image obtain more prominent features to adapt to the model. Finally, we used the KiTS19 data set and took the size of the kidney tumor as the category judgment standard to enhance the small sample data set to balance the sample data set. We tested the segmentation effect of the model at different convolution and depths, and we got scored a 0.948 kidney Dice and a 0.911 tumor Dice results in a 0.930 composite score, showing a good segmentation effect.

3D multi-scale residual fully convolutional neural network for segmentation of extremely large-sized kidney tumor

BACKGROUND AND OBJECTIVE

We propose a novel deep neural network, the 3D Multi-Scale Residual Fully Convolutional Neural Network (3D-MS-RFCNN) to improve segmentation in extremely large-sized kidney tumors.

METHOD

The multi-scale approach with a deep neural network is applied to capture global contextual features. Our method, 3D-MS-RFCNN, consists of two encoders and one decoder as a single complete network. One of the encoders is designed for capturing global contextual information by using the low-resolution, down-sampled data from input images. In the decoder, features from the encoder for global contextual features are concatenated with up-sampled features from the previous layer and features from the other encoder. Ensemble learning strategy is also applied.

RESULTS

We evaluated the performance of our proposed method using the KiTS public dataset and the in-house hospital dataset. When compared with the state-of-the-art method, Res3D U-Net, our model, 3D-MS-RFCNN, demonstrated greater accuracy (0.9390 dice score for KiTS dataset and 0.8575 dice score for external dataset) for segmenting extremely large-sized kidney tumors.

CONCLUSIONS

Our proposed network shows significantly improved segmentation performance of extremely large-sized targets. This study can be usefully employed in the field of medical image analysis.

Automated and robust organ segmentation for 3D-based internal dose calculation

PURPOSE

In this work, we address image segmentation in the scope of dosimetry using deep learning and make three main contributions: (a) to extend and optimize the architecture of an existing convolutional neural network (CNN) in order to obtain a fast, robust and accurate computed tomography (CT)-based organ segmentation method for kidneys and livers; (b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; and (c) to evaluate dosimetry results obtained using automated organ segmentation in comparison with manual segmentation done by two independent experts.

METHODS

We adapted a performant deep learning approach using CT-images to delineate organ boundaries with sufficiently high accuracy and adequate processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the activity values from quantitatively reconstructed SPECT images for "volumetric"/3D dosimetry. The resulting activities were used to perform dosimetry calculations with the kidneys as source organs.

RESULTS

The computational expense of the algorithm was sufficient for clinical daily routine, required minimum pre-processing and performed with acceptable accuracy a Dice coefficient of [Formula: see text] for liver segmentation and of [Formula: see text] for kidney segmentation, respectively. In addition, kidney self-absorbed doses calculated using automated segmentation differed by [Formula: see text] from dosimetry performed by two medical physicists in 8 patients.

CONCLUSION

The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radiopharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmentation methodology based on CT images accelerates organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images. Trial registration EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13 .

Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks

Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, the segmentation of renal structures can be challenging due to the limited field-of-view (FOV) and variability among patients. In this paper, we propose a method to automatically label the renal cortex, the medulla and pelvicalyceal system. First, we retrieved 45 clinically-acquired deidentified arterial phase CT scans (45 patients, 90 kidneys) without diagnosis codes (ICD-9) involving kidney abnormalities. Second, an interpreter performed manual segmentation to pelvis, medulla and cortex slice-by-slice on all retrieved subjects under expert supervision. Finally, we proposed a patch-based deep neural networks to automatically segment renal structures. Compared to the automatic baseline algorithm (3D U-Net) and conventional hierarchical method (3D U-Net Hierarchy), our proposed method achieves improvement of 0.7968 to 0.6749 (3D U-Net), 0.7482 (3D U-Net Hierarchy) in terms of mean Dice scores across three classes (p-value < 0.001, paired t-tests between our method and 3D U-Net Hierarchy). In summary, the proposed algorithm provides a precise and efficient method for labeling renal structures.

Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants

The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.

Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning

OBJECTIVES

To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi-automated fashion for tumour-to-cortex peak early-phase enhancement ratio (PEER) in order to differentiate CD117(+) oncocytoma from the chromophobe subtype of renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on computed tomography imaging.

METHODS

The CNN was trained and validated to identify the kidney + tumour areas in images from 192 patients. The tumour type was differentiated through automated measurement of PEER after manual segmentation of tumours. The performance of this diagnostic model was compared with that of manual expert identification and tumour pathology with regard to accuracy, sensitivity and specificity, along with the root-mean-square error (RMSE), for the remaining 20 patients with CD117(+) oncocytoma or ChRCC.

RESULTS

The mean ± sd Dice similarity score for segmentation was 0.66 ± 0.14 for the CNN model to identify the kidney + tumour areas. PEER evaluation achieved accuracy of 95% in tumour type classification (100% sensitivity and 89% specificity) compared with the final pathology results (RMSE of 0.15 for PEER ratio).

CONCLUSIONS

We have shown that deep learning could help to produce reliable discrimination of CD117(+) benign oncocytoma and malignant ChRCC through PEER measurements obtained by computer vision.

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans

We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. First, some organs (e.g., the pancreas), are highly variable in both anatomy and geometry, and thus very difficult to depict. Second, the neoplasms often vary a lot in its size, shape, as well as its location within the organ. Third, the targets (organs and neoplasms) can be considerably small compared to the human body, and so standard deep networks for segmentation are often less sensitive to these targets and thus predict less accurately especially around their boundaries. In this paper, we present an end-to-end framework named recurrent saliency transformation network (RSTN) for segmenting tiny and/or variable targets. The RSTN is a coarse-to-fine approach that uses prediction from the first (coarse) stage to shrink the input region for the second (fine) stage. A saliency transformation module is inserted between these two stages so that 1) the coarse-scaled segmentation mask can be transferred as spatial weights and applied to the fine stage and 2) the gradients can be back-propagated from the loss layer to the entire network so that the two stages are optimized in a joint manner. In the testing stage, we perform segmentation iteratively to improve accuracy. In this extended journal paper, we allow a gradual optimization to improve the stability of the RSTN, and introduce a hierarchical version named H-RSTN to segment tiny and variable neoplasms such as pancreatic cysts. Experiments are performed on several CT datasets including a public pancreas segmentation dataset, our own multi-organ dataset, and a cystic pancreas dataset. In all these cases, the RSTN outperforms the baseline (a stage-wise coarse-to-fine approach) significantly. Confirmed by the radiologists in our team, these promising segmentation results can help early diagnosis of pancreatic cancer. The code and pre-trained models of our project were made available at https://github.com/198808xc/OrganSegRSTN.

Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams

This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgical simulation of partial nephrectomy surgery, we must obtain information on the renal arteries and the renal vascular dominant regions. We propose a fully automatic segmentation method that combines a neural network and tensor-based graph-cut methods to precisely extract the kidney and renal arteries. First, we use a convolutional neural network to localize the kidney regions and extract tiny renal arteries with a tensor-based graph-cut method. Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries. The accuracy of kidney segmentation in 27 cases with 8-fold cross validation reached a Dice score of 95%. The accuracy of renal artery segmentation in 8 cases obtained a centerline overlap ratio of 80%. Each partition region corresponds to a renal vascular dominant region. The final dominant-region estimation accuracy achieved a Dice coefficient of 80%. A clinical application showed the potential of our proposed estimation approach in a real clinical surgical environment. Further validation using large-scale database is our future work.

Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling

Traumatic abdominal injury can lead to multiple complications including laceration of major organs such as kidneys. Contrast-enhanced Computed Tomography (CT) is the primary imaging modality for evaluating kidney injury. However, the traditional visual examination of CT scans is time consuming, non-quantitative, prone to human error, and costly. In this work we propose a kidney segmentation method using machine learning and active contour modeling. We first detect an initialization mask inside the kidney and then evolve its boundary. This model is specifically developed and evaluated on trauma cases. Our experimental results show the average recall score of 92.6% and average Dice similarity value of 88.9%.

Automated Region of Interest Detection Method in Scintigraphic Glomerular Filtration Rate Estimation

The glomerular filtration rate (GFR) is a crucial index to measure renal function. In daily clinical practice, the GFR can be estimated using the Gates method, which requires the clinicians to define the region of interest (ROI) for the kidney and the corresponding background in dynamic renal scintigraphy. The manual placement of ROIs to estimate the GFR is subjective and labor-intensive, however, making it an undesirable and unreliable process. This work presents a fully automated ROI detection method to achieve accurate and robust GFR estimations. After image preprocessing, the ROI for each kidney was delineated using a shape prior constrained level set (spLS) algorithm and then the corresponding background ROIs were obtained according to the defined kidney ROIs. In computer simulations, the spLS method had the best performance in kidney ROI detection compared with the previous threshold method (threshold) and the Chan-Vese level set (cvLS) method. In further clinical applications, 223 sets of ^99mTc-diethylenetriaminepentaacetic acid renal scintigraphic images from patients with abnormal renal function were reviewed. Compared with the former ROI detection methods (threshold and cvLS), the GFR estimations based on the ROIs derived by the spLS method had the highest consistency and correlations (r = 0.98, p < 0.001) with the reference estimated by experienced physicians. The results indicate that the proposed automated ROI detection method has great potential in automated ROI detection for accurate and robust GFR estimation in dynamic renal scintigraphy.

3D marker-controlled watershed for kidney segmentation in clinical CT exams

Background

Image segmentation is an essential and non trivial task in computer vision and medical image analysis. Computed tomography (CT) is one of the most accessible medical examination techniques to visualize the interior of a patient’s body. Among different computer-aided diagnostic systems, the applications dedicated to kidney segmentation represent a relatively small group. In addition, literature solutions are verified on relatively small databases. The goal of this research is to develop a novel algorithm for fully automated kidney segmentation. This approach is designed for large database analysis including both physiological and pathological cases.

Methods

This study presents a 3D marker-controlled watershed transform developed and employed for fully automated CT kidney segmentation. The original and the most complex step in the current proposition is an automatic generation of 3D marker images. The final kidney segmentation step is an analysis of the labelled image obtained from marker-controlled watershed transform. It consists of morphological operations and shape analysis. The implementation is conducted in a MATLAB environment, Version 2017a, using i.a. Image Processing Toolbox. 170 clinical CT abdominal studies have been subjected to the analysis. The dataset includes normal as well as various pathological cases (agenesis, renal cysts, tumors, renal cell carcinoma, kidney cirrhosis, partial or radical nephrectomy, hematoma and nephrolithiasis). Manual and semi-automated delineations have been used as a gold standard. Wieclawek Among 67 delineated medical cases, 62 cases are ‘Very good’, whereas only 5 are ‘Good’ according to Cohen’s Kappa interpretation. The segmentation results show that mean values of Sensitivity, Specificity, Dice, Jaccard, Cohen’s Kappa and Accuracy are 90.29, 99.96, 91.68, 85.04, 91.62 and 99.89% respectively. All 170 medical cases (with and without outlines) have been classified by three independent medical experts as ‘Very good’ in 143–148 cases, as ‘Good’ in 15–21 cases and as ‘Moderate’ in 6–8 cases.

Conclusions

An automatic kidney segmentation approach for CT studies to compete with commonly known solutions was developed. The algorithm gives promising results, that were confirmed during validation procedure done on a relatively large database, including 170 CTs with both physiological and pathological cases.

A Multiorgan Segmentation Model for CT Volumes via Full Convolution-Deconvolution Network

We propose a model with two-stage process for abdominal segmentation on CT volumes. First, in order to capture the details of organs, a full convolution-deconvolution network (FCN-DecNet) is constructed with multiple new unpooling, deconvolutional, and fusion layers. Then, we optimize the coarse segmentation results of FCN-DecNet by multiscale weights probabilistic atlas (MS-PA), which uses spatial and intensity characteristic of atlases. Our coarse-fine model takes advantage of intersubject variability, spatial location, and gray information of CT volumes to minimize the error of segmentation. Finally, using our model, we extract liver, spleen, and kidney with Dice index of 90.1 ± 1%, 89.0 ± 1.6%, and 89.0 ± 1.3%, respectively.

Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease

Magnetic resonance imaging (MRI) examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Height-adjusted TKV (HtTKV) has become the gold-standard imaging biomarker for ADPKD progression at early stages of the disease when estimated glomerular filtration rate (eGFR) is still normal. However, HtTKV does not take advantage of the wealth of information provided by MRI. Here we tested whether image texture features provide additional insights into the ADPKD kidney that may be used as complementary information to existing biomarkers. A retrospective cohort of 122 patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study was identified who had T2-weighted MRIs and eGFR values over 70 mL/min/1.73m² at the time of their baseline scan. We computed nine distinct image texture features for each patient. The ability of each feature to predict subsequent progression to CKD stage 3A, 3B, and 30% reduction in eGFR at eight-year follow-up was assessed. A multiple linear regression model was developed incorporating age, baseline eGFR, HtTKV, and three image texture features identified by stability feature selection (Entropy, Correlation, and Energy). Including texture in a multiple linear regression model (predicting percent change in eGFR) improved Pearson correlation coefficient from -0.51 (using age, eGFR, and HtTKV) to -0.70 (adding texture). Thus, texture analysis offers an approach to refine ADPKD prognosis and should be further explored for its utility in individualized clinical decision making and outcome prediction.

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study

Kidney volume is an important parameter in clinical practice, and accurate assessment of kidney volume is vital. The aim of the present study was to evaluate the effect of various environments, tube voltages, tube currents and slice thicknesses on the accuracy of a novel segmentation software in determining renal volume on computed tomography (CT) images. The volumes of potatoes and porcine kidneys were measured on CT images and compared with the actual volumes, which were determined by a water displacement method. CT scans were performed under various situations, including different environments (air or oil); tube voltages/tube currents (80 kVp/200 mAs, 120 kVp/200 mAs, 120 kVp/100 mAs); and reconstructed slice thicknesses (0.75 or 1.5 mm). Percentage errors (PEs) relative to the reference standards were calculated. In addition, attenuation and image noise under different CT scanning parameters were compared. Student's t-test was also used to analyze the effect of various conditions on image quality and volume measurements. The results indicated that the volumes measured in oil were closer to the actual volumes (P<0.05). Furthermore, attenuation and image noise significantly increased when using a tube voltage of 80 kVp, while the mean PEs between 120 and 80 kVp voltages were not significantly different. The mean PEs were greater when using a tube current of 100 mAs compared with a current of 200 mAs (P<0.05). In addition, the volumes measured on 1.5 mm slice thickness were closer to the actual volumes (P<0.05). In conclusion, different environments, tube currents and slice thicknesses may affect the volume measurements. In the present study, the most accurate volume measurements were obtained at 120 kVp/200 mAs and a slice thickness of 1.5 mm.

3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest

In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds.

Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease

BACKGROUND AND OBJECTIVES

Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed.

RESULTS

Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable.

CONCLUSIONS

We have developed a fully automated method for segmentation of kidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good agreement with that of manual method.

Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images

Due to recent technical advancements of three-dimensional ultrasound imaging systems, applications of this imaging modality have been expanding from the fetal imaging to cardiac- and abdominal-diagnosis. Among all internal organs, diagnosing the kidney has a paramount importance for rapid bedside treatment of trauma and kidney stone patients using ultrasound images. Although three-dimensional ultrasound provides higher level of structural information of kidneys, manual kidney diagnosis using three-dimensional ultrasound images requires a highly trained medical staff, due to the extensive visual complexity which three-dimensional images contain. Therefore, computer aided automated kidney diagnosis becomes very essential. Due to the challenging problems of ultrasound images, such as speckle noise and inhomogeneous intensity profile, kidney segmentation in three-dimensional ultrasound images has not been sufficiently investigated by researchers. In this paper, we first propose a new automated kidney detection approach using three-dimensional Morison's pouch ultrasound images. Then, we proposed a shape-based method to segment the detected kidneys. A preprocessing step is utilized to overcome the ultrasound challenges. Based on a set of 14 ultrasound volumes, we have evaluated the detection rate of our proposed kidney detection approach which is 92.86%. For kidney segmentation, we compared our proposed method with an existing approach, and the performed statistical analysis strongly validates the superiority of our proposed method with p = 0.000032.

Automatic kidney segmentation in CT images based on multi-atlas image registration

Kidney segmentation is an important step for computer-aided diagnosis or treatment in urology. In this paper, we present an automatic method based on multi-atlas image registration for kidney segmentation. The method mainly relies on a two-step framework to obtain coarse-to-fine segmentation results. In the first step, down-sampled patient image is registered with a set of low-resolution atlas images. A coarse kidney segmentation result is generated to locate the left and right kidneys. In the second step, the left and right kidneys are cropped from original images and aligned with another set of high-resolution atlas images to obtain the final results respectively. Segmentation results from 14 CT angiographic (CTA) images show that our proposed method can segment the kidneys with a high accuracy. The average Dice similarity coefficient and surface-to-surface distance between segmentation results and reference standard are 0.952 and 0.913mm. Furthermore, the kidney segmentation in CT urography (CTU) and CTA images of 12 patients were performed to show the feasibility of our method in CTU images.

Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression

BACKGROUND

Renal imaging examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) patients. TKV has become the gold-standard image biomarker for ADPKD progression at early stages of the disease and is used in clinical trials to characterize treatment efficacy. Automated methods to segment the kidneys and measure TKV are desirable because of the long time requirement for manual approaches such as stereology or planimetry tracings. However, ADPKD kidney segmentation is complicated by a number of factors, including irregular kidney shapes and variable tissue signal at the kidney borders.

METHODS

We describe an image processing approach that overcomes these problems by using a baseline segmentation initialization to provide automatic segmentation of follow-up scans obtained years apart. We validated our approach using 20 patients with complete baseline and follow-up T1-weighted magnetic resonance images. Both manual tracing and stereology were used to calculate TKV, with two observers performing manual tracings and one observer performing repeat tracings. Linear correlation and Bland-Altman analysis were performed to compare the different approaches.

RESULTS

Our automated approach measured TKV at a level of accuracy (mean difference ± standard error = 0.99 ± 0.79%) on par with both intraobserver (0.77 ± 0.46%) and interobserver variability (1.34 ± 0.70%) of manual tracings. All approaches had excellent agreement and compared favorably with ground-truth manual tracing with interobserver, stereological and automated approaches having 95% confidence intervals ∼ ± 100 mL.

CONCLUSIONS

Our method enables fast, cost-effective and reproducible quantification of ADPKD progression that will facilitate and lower the costs of clinical trials in ADPKD and other disorders requiring accurate, longitudinal kidney quantification. In addition, it will hasten the routine use of TKV as a prognostic biomarker in ADPKD.

Computer-aided detection of exophytic renal lesions on non-contrast CT images

Renal lesions are important extracolonic findings on computed tomographic colonography (CTC). They are difficult to detect on non-contrast CTC images due to low image contrast with surrounding objects. In this paper, we developed a novel computer-aided diagnosis system to detect a subset of renal lesions, exophytic lesions, by (1) exploiting efficient belief propagation to segment kidneys, (2) establishing an intrinsic manifold diffusion on kidney surface, (3) searching for potential lesion-caused protrusions with local maximum diffusion response, and (4) exploring novel shape descriptors, including multi-scale diffusion response, with machine learning to classify exophytic renal lesions. Experimental results on the validation dataset with 167 patients revealed that manifold diffusion significantly outperformed conventional shape features (p<1e-3) and resulted in 95% sensitivity with 15 false positives per patient for detecting exophytic renal lesions. Fivefold cross-validation also demonstrated that our method could stably detect exophytic renal lesions. These encouraging results demonstrated that manifold diffusion is a key means to enable accurate computer-aided diagnosis of renal lesions.

A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces

Three-dimensional (3D) medical imaging has been incorporated in routine clinical practice, since the required infrastructure has become increasingly affordable. New algorithms and applications are needed to serve the additional image processing and analysis functions in 3D space. In this work we propose a system for semi-automatic modeling and segmentation of elongated salient and anatomical objects in 3D medical images. The proposed methodology is based on a novel mathematical formalization of a well-known class of geometric primitives, namely generalized cylinders (GCs), which exhibits advantages over the existing parametric definition. Since the anatomical objects have to be modeled by their intersection with the transverse image planes, the proposed methodology includes also a new seeded region growing (SRG) segmentation algorithm for ellipse detection in 2D images, based on a priori shape knowledge. Finally, the resulting GC model is used to initialize an active surface (AS) segmentation method, in order to accurately delineate the required object. In this work we present the proposed algorithms in detail, along with the evaluation of the accuracy of the model-based segmentation by experts. Results show that elongated objects like the aorta and the trachea may be segmented with sensitivity between 90% and 95%. The proposed SRG-ellipse detector requires minimal user-initialization and its executions requires only few seconds for each image slice on an average laptop. The evolution of the AS requires less than one second per iteration for a typical CT image. Comparisons are provided with state of the art semi-automatic medical image processing software, which validate the merit of the proposed work.

Anatomical structures segmentation by spherical 3D ray casting and gradient domain editing

Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.

Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search

In this paper, we present an automatic renal cortex segmentation approach using the implicit shape registration and novel multiple surfaces graph search. The proposed approach is based on a hierarchy system. First, the whole kidney is roughly initialized using an implicit shape registration method, with the shapes embedded in the space of Euclidean distance functions. Second, the outer and inner surfaces of renal cortex are extracted utilizing multiple surfaces graph searching, which is extended to allow for varying sampling distances and physical constraints to better separate the renal cortex and renal column. Third, a renal cortex refining procedure is applied to detect and reduce incorrect segmentation pixels around the renal pelvis, further improving the segmentation accuracy. The method was evaluated on 17 clinical computed tomography scans using the leave-one-out strategy with five metrics: Dice similarity coefficient (DSC), volumetric overlap error (OE), signed relative volume difference (SVD), average symmetric surface distance (D(avg)), and average symmetric rms surface distance (D(rms)). The experimental results of DSC, OE, SVD, D(avg) , and D(rms) were 90.50% ± 1.19%, 4.38% ± 3.93%, 2.37% ± 1.72%, 0.14 mm ± 0.09 mm , and 0.80 mm ± 0.64 mm, respectively. The results showed the feasibility, efficiency, and robustness of the proposed method.

Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images

Background

For some scoliotic patients the spinal instrumentation is inevitable. Among these patients, those with stiff curvature will need thoracoscopic disk resection. The removal of the intervertebral disk with only thoracoscopic images is a tedious and challenging task for the surgeon. With computer aided surgery and 3D visualisation of the interverterbral disk during surgery, surgeons will have access to additional information such as the remaining disk tissue or the distance of surgical tools from critical anatomical structures like the aorta or spinal canal. We hypothesized that automatically extracting 3D information of the intervertebral disk from MR images would aid the surgeons to evaluate the remaining disk and would add a security factor to the patient during thoracoscopic disk resection.

Methods

This paper presents a quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images. The automatic segmentation method is based on the watershed technique and morphological operators. The 3D Dice Similarity Coefficient (DSC) is the main statistical metric used to validate the automatically detected preoperative disk volumes. The automatic detections of intervertebral disks of real clinical MR images are compared to manual segmentation done by clinicians.

Results

Results show that depending on the type of MR acquisition sequence, the 3D DSC can be as high as 0.79 (±0.04). These 3D results are also supported by a 2D quantitative evaluation as well as by robustness and variability evaluations. The mean discrepancy (in 2D) between the manual and automatic segmentations for regions around the spinal canal is of 1.8 (±0.8) mm. The robustness study shows that among the five factors evaluated, only the type of MRI acquisition sequence can affect the segmentation results. Finally, the variability of the automatic segmentation method is lower than the variability associated with manual segmentation performed by different physicians.

Conclusions

This comprehensive evaluation of the automatic segmentation and 3D reconstruction of intervertebral disks shows that the proposed technique used with specific MRI acquisition protocol can detect intervertebral disk of scoliotic patient. The newly developed technique is promising for clinical context and can eventually help surgeons during thoracoscopic intervertebral disk resection.

An automatic method for renal cortex segmentation on CT images: evaluation on kidney donors

RATIONALE AND OBJECTIVES

The aims of this study were to develop and validate an automated method to segment the renal cortex on contrast-enhanced abdominal computed tomographic images from kidney donors and to track cortex volume change after donation.

MATERIALS AND METHODS

A three-dimensional fully automated renal cortex segmentation method was developed and validated on 37 arterial phase computed tomographic data sets (27 patients, 10 of whom underwent two computed tomographic scans before and after nephrectomy) using leave-one-out strategy. Two expert interpreters manually segmented the cortex slice by slice, and linear regression analysis and Bland-Altman plots were used to compare automated and manual segmentation. The true-positive and false-positive volume fractions were also calculated to evaluate the accuracy of the proposed method. Cortex volume changes in 10 subjects were also calculated.

RESULTS

The linear regression analysis results showed that the automated and manual segmentation methods had strong correlations, with Pearson's correlations of 0.9529, 0.9309, 0.9283, and 0.9124 between intraobserver variation, interobserver variation, automated and user 1, and automated and user 2, respectively (P < .001 for all analyses). The Bland-Altman plots for cortex segmentation also showed that the automated and manual methods had agreeable segmentation. The mean volume increase of the cortex for the 10 subjects was 35.1 ± 13.2% (P < .01 by paired t test). The overall true-positive and false-positive volume fractions for cortex segmentation were 90.15 ± 3.11% and 0.85 ± 0.05%. With the proposed automated method, the time for cortex segmentation was reduced from 20 minutes for manual segmentation to 2 minutes.

CONCLUSIONS

The proposed method was accurate and efficient and can replace the current subjective and time-consuming manual procedure. The computer measurement confirms the volume of renal cortex increases after kidney donation.

Prior shape level set segmentation on multistep generated probability maps of MR datasets for fully automatic kidney parenchyma volumetry

Fully automatic 3-D segmentation techniques for clinical applications or epidemiological studies have proven to be a very challenging task in the domain of medical image analysis. 3-D organ segmentation on magnetic resonance (MR) datasets requires a well-designed segmentation strategy due to imaging artifacts, partial volume effects, and similar tissue properties of adjacent tissues. We developed a 3-D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation. The probability map quality is improved in a multistep refinement approach. An extended prior shape level set segmentation method is then applied on the refined probability maps. The segmentation quality is improved by incorporating an exterior cortex edge alignment technique using cortex probability maps. In contrast to previous approaches, we combine several relevant kidney parenchyma features in a sequence of segmentation techniques for successful parenchyma delineation on native MR datasets. Furthermore, the proposed method is able to recognize and exclude parenchymal cysts from the parenchymal volume. We analyzed four different quality measures showing better results for right parenchymal tissue than for left parenchymal tissue due to an incorporated liver part removal in the segmentation framework. The results show that the outer cortex edge alignment approach successfully improves the quality measures.

Comparison and assessment of semi-automatic image segmentation in computed tomography scans for image-guided kidney surgery

PURPOSE

Image segmentation is integral to implementing intraoperative guidance for kidney tumor resection. Results seen in computed tomography (CT) data are affected by target organ physiology as well as by the segmentation algorithm used. This work studies variables involved in using level set methods found in the Insight Toolkit to segment kidneys from CT scans and applies the results to an image guidance setting.

METHODS

A composite algorithm drawing on the strengths of multiple level set approaches was built using the Insight Toolkit. This algorithm requires image contrast state and seed points to be identified as input, and functions independently thereafter, selecting and altering method and variable choice as needed.

RESULTS

Semi-automatic results were compared to expert hand segmentation results directly and by the use of the resultant surfaces for registration of intraoperative data. Direct comparison using the Dice metric showed average agreement of 0.93 between semi-automatic and hand segmentation results. Use of the segmented surfaces in closest point registration of intraoperative laser range scan data yielded average closest point distances of approximately 1 mm. Application of both inverse registration transforms from the previous step to all hand segmented image space points revealed that the distance variability introduced by registering to the semi-automatically segmented surface versus the hand segmented surface was typically less than 3 mm both near the tumor target and at distal points, including subsurface points.

CONCLUSIONS

Use of the algorithm shortened user interaction time and provided results which were comparable to the gold standard of hand segmentation. Further, the use of the algorithm's resultant surfaces in image registration provided comparable transformations to surfaces produced by hand segmentation. These data support the applicability and utility of such an algorithm as part of an image guidance workflow.

Renal cortex segmentation using optimal surface search with novel graph construction

In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10% and 0.08%, respectively. The experimental results demonstrate the effectiveness of the proposed method.

Automated measurement method and tool of glomerular filtration rate using triphasic helical computed tomography images

OBJECTIVES

To introduce an automated method to perform computed tomography-based glomerular filtration rate (CT GFR) measurement using the 2-point Patlak plot technique and triphasic helical CT images. We also evaluated the correlation between our automated method and the manual measurement results, as well as the results from cystatin C.

METHODS

The present study included 25 patients without an acute renal disorder. The CT scan protocol consisted of an unenhanced CT examination followed by 2 contrast-enhanced CT examinations in the arterial and parenchymal phase. Between the noncontrast scan and the arterial scan, 7 dynamic scans were obtained to provide more data on the arterial input function. The 2-point Patlak plot technique was used in the automated and manual measurement methods. For each patient, the cystatin C level, determined in blood samples, was used as a reference.

RESULTS

The correlation between our automated method and the cystatin C method in 25 patients was 0.8029 (P < .001). The correlation between the manually implemented CT GFR method and the cystatin C method was 0.8287 (P < .001). A strong correlation (r = .9518, P < .001) was seen between the automated and manual measurements using the same model; however, the automated process can be finished within 2 minutes.

CONCLUSIONS

The automated CT GFR measurement method could potentially be used because of its highly improved efficiency. Moreover, it would avoid the use of a differential renal nuclear study. This would be helpful for predicting the residual GFR after nephrectomy and could also be used to predict the residual GFR after partial nephrectomy for tumor or stone treatment.

Semiautomated segmentation of kidney from high-resolution multidetector computed tomography images using a graph-cuts technique

OBJECTIVES

To develop a semiautomated segmentation method based on a graph-cuts technique from multidetector computed tomography images for kidney segmentation and to evaluate and compare it with the conventional manual delineation segmentation method.

MATERIALS AND METHODS

We have developed a semiautomated segmentation method that is based on a graph-cuts technique with enhanced features including automated seed growing. Multidetector computed tomography images were obtained from 15 consecutive patients who were being evaluated as possible living donors for kidney transplant. Two observers independently performed the segmentation of the kidney from the multidetector computed tomography images using the manual and semiautomated methods. The efficiency of the 2 methods were measured by segmentation processing times and then compared. The interobserver and method reproducibility was determined by Dice similarity coefficient (DSC), which measures how closely 2 segmented volumes overlap geometrically and the coefficient of variation of volume measurements.

RESULTS

The mean segmentation processing time was (manual vs semiautomated, P < 0.001) 96.8 +/- 13.6 vs 13.7 +/- 3.5 minutes for observer 1 and 44.3 +/- 4.7 vs 16.2 +/- 5.1 minutes for observer 2. The mean interobserver reproducibility was (manual vs semiautomated, P < 0.001) 93.6 +/- 1.6% vs 97.3 +/- 0.9% for DSC and 5.3 +/- 2.6% vs 2.2 +/- 1.3% for coefficient of variation, indicating higher interobserver reproducibility with the semiautomated than manual method. The agreement between the 2 segmentation methods was high (mean intermethod DSC 95.8 +/- 1.0% and 94.9 +/- 0.8%) for both observers.

CONCLUSIONS

The semiautomated method was significantly more efficient and reproducible than the manual delineation method for segmentation of kidney from MDCT images.

Dynamic-threshold level set method for volumetry of porcine kidney in CT images in vivo and ex vivo assessment of the accuracy of volume measurement

RATIONALE AND OBJECTIVE

We sought to assess the accuracy of a novel computerized volumetry method, called dynamic-thresholding (DT) level set, in determining the renal volume of pigs in CT images on the basis of in vivo and ex vivo reference standards.

METHODS AND MATERIALS

Eight Yorkshire breed anesthetized pigs (weight range 45-50 kg) were scanned on a 64-slice multidetector CT scanner (Sensation 64; Siemens) after injection of an iodinated (300 mg I/ml) contrast agent through an IV cannula. The kidneys of the pigs were then surgically resected and scanned by CT in the same manner. Both in vivo and ex vivo CT images were subjected to our computerized volumetry using DT level set method. The resulting volumes of the kidneys were compared with in vivo and ex vivo reference standards: the former was established by manual contouring of the kidneys on the CT images by an experienced radiologist, and the latter was established as the water displacement volume of the resected kidney.

RESULTS

The comparisons of the in vivo and ex vivo measurements by our volumetric scheme with the associated reference standards yielded a mean difference of 1.73 +/- 1.24% and 3.38 +/- 2.51%, respectively. The correlation coefficients were 0.981 and 0.973 for in vivo and ex vivo comparisons, respectively. The mean difference between in vivo and ex vivo reference standards was 5.79 +/- 4.26%, and the correlation coefficient between the two standards was 0.760.

CONCLUSION

Our computerized volumetry using the DT level set method can provide accurate in vivo and ex vivo measurements of kidney volume, despite a large difference between the two reference standards. This technique can be employed in human subjects for the determination of renal volume for preoperative surgical planning and assessment of oncology treatment.