Automatic Segmentation of Kidneys and Kidney Tumors: The KiTS19 International Challenge

Purpose: Clinicians rely on imaging features to calculate complexity of renal masses based on validated scoring systems. These scoring methods are labor-intensive and are subjected to interobserver variability. Artificial intelligence has been increasingly utilized by the medical community to solve such issues. However, developing reliable algorithms is usually time-consuming and costly. We created an international community-driven competition (KiTS19) to develop and identify the best system for automatic segmentation of kidneys and kidney tumors in contrast CT and report the results.Methods: A training and test set of CT scans that was manually annotated by trained individuals were generated from consecutive patients undergoing renal surgery for whom demographic, clinical and outcome data were available. The KiTS19 Challenge was a machine learning competition hosted on grand-challenge.org in conjunction with an international conference. Teams were given 3 months to develop their algorithm using a full-annotated training set of images and an unannotated test set was released for 2 weeks from which average Sørensen-Dice coefficient between kidney and tumor regions were calculated across all 90 test cases.Results: There were 100 valid submissions that were based on deep neural networks but there were differences in pre-processing strategies, architectural details, and training procedures. The winning team scored a 0.974 kidney Dice and a 0.851 tumor Dice resulting in 0.912 composite score. Automatic segmentation of the kidney by the participating teams performed comparably to expert manual segmentation but was less reliable when segmenting the tumor.Conclusion: Rapid advancement in automated semantic segmentation of kidney lesions is possible with relatively high accuracy when the data is released publicly, and participation is incentivized. We hope that our findings will encourage further research that would enable the potential of adopting AI into the medical field.

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Consumerism for Neurobiological Disorders: An Assessment

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462300010941 ◽

1996 ◽

Vol 12 (4) ◽

pp. 644-656

Author(s):

Richard E. Peschel ◽

Enid Peschel

Keyword(s):

Mental Health ◽

Health Care ◽

Mental Health Care ◽

Physical Nature ◽

Outcome Data ◽

Public Understanding ◽

Medical Community ◽

Treatment Programs ◽

Equitable Treatment

AbstractConsumerism is a growing phenomenon in U.S. health care, yet its exercise is still inhibited by powerful forces within the medical community. Despite the neuroscientific framework that stresses the commonalities between mental and physical illness, consumerism is even more problematic and difficult in mental health care than in other areas of health care. People with severe mental illness and their advocates must contend with limited public understanding of neurobiological disorders, poor definitions of effective treatment, and a paucity of outcome data, especially from prospective randomized and long-term studies. The only clear way for consumerism to grow in mental health care is for its advocates to align themselves with the neuroscientific revolution and to demand that effective and equitable treatment programs be created based on the documented evidence of the physical nature of neurobiological disorders.

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Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

10.21203/rs.3.rs-951065/v1 ◽

2021 ◽

Author(s):

Sang-Heon Lim ◽

Young Jae Kim ◽

Yeon-Ho Park ◽

Doojin Kim ◽

Kwang Gi Kim ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Automatic Segmentation ◽

External Validation ◽

Three Dimensional ◽

Semantic Segmentation ◽

Quantitative Information ◽

Similarity Coefficients ◽

Internal Validation ◽

Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

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Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

Ultrasonic Imaging ◽

10.1177/01617346211042526 ◽

2021 ◽

pp. 016173462110425

Author(s):

Jianing Xi ◽

Jiangang Chen ◽

Zhao Wang ◽

Dean Ta ◽

Bing Lu ◽

...

Keyword(s):

Congenital Anomaly ◽

Large Scale ◽

Automatic Segmentation ◽

Receptive Fields ◽

Semantic Segmentation ◽

Attention Mechanism ◽

Scale Model ◽

Ultrasound Images ◽

Multi Scale ◽

Task Irrelevant

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

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ABC of intensive care: Outcome data and scoring systems

BMJ ◽

10.1136/bmj.319.7204.241 ◽

1999 ◽

Vol 319 (7204) ◽

pp. 241-244 ◽

Cited By ~ 85

Author(s):

K. Gunning ◽

K. Rowan

Keyword(s):

Intensive Care ◽

Scoring Systems ◽

Outcome Data ◽

Care Outcome

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Cascaded U-net for Kidney and Tumor Segmentation from CT volumes

Artificial Intelligence in Oncology ◽

10.52454/aio.v2i1.8 ◽

2020 ◽

Vol 2 (1) ◽

pp. 004-008

Author(s):

Asha K Kumaraswamy ◽

Chandrashekar Patil

Keyword(s):

Semantic Segmentation ◽

Ct Images ◽

Difficult Problem ◽

Training Data ◽

Tumor Segmentation ◽

Computer Assisted ◽

Kidney Tumors ◽

Cross Sectional ◽

Morphological Heterogeneity ◽

Enhanced Computed Tomography

Contrast-enhanced Computed Tomography (CT) imaging is most useful tool in diagnosing and locating the kidney lesions. An automated kidney and tumor segmentation are very helpful because it can provide the precise information about the location and size of lesions which can be used in quantitative analysis of the tumor. Semantic segmentation of kidney is very challenging as it requires large dataset for training and its morphological heterogeneity makes it a difficult problem. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) has publicly released a 210 cross sectional CT images with kidney tumors along with corresponding semantic segmentation masks. In this work we proposed a novel two stage 2D segmentation method to automatically segment kidney and tumor using the combination of Unet++ and squeeze and excite approach. The proposed network is trained in keras framework. Our method achieves a dice score of 0.98 and 0.965 with kidney and tumor respectively on training data and the results demonstrates the accuracy of our proposed method. Proposed method was able to segment kidney and tumor from abdominal CT images which can provide the exact location and size of the tumor. This information can also be used to analyze treatment response.

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Concrete Cracks Detection Based on FCN with Dilated Convolution

Applied Sciences ◽

10.3390/app9132686 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2686 ◽

Cited By ~ 15

Author(s):

Jianming Zhang ◽

Chaoquan Lu ◽

Jin Wang ◽

Lei Wang ◽

Xiao-Guang Yue

Keyword(s):

Crack Detection ◽

Receptive Fields ◽

Semantic Segmentation ◽

Concrete Surface ◽

Input Image ◽

Feature Maps ◽

Test Set ◽

Dilated Convolution ◽

Fully Convolutional Networks ◽

Segmentation Task

In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the input image, designed the dilated convolutions with different dilation rates to extract the feature maps of different receptive fields, and fused the extracted features from multiple branches. Then, we exploited the stacked deconvolution to do up-sampling operator in the fused feature maps. Finally, we used the SoftMax function to classify the feature maps at the pixel level. In order to verify the validity of the model, we introduced the commonly used evaluation indicators of semantic segmentation: Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU). The experimental results show that the proposed model converges faster and has better generalization performance on the test set by introducing dilated convolutions with different dilation rates and a multi-branch fusion strategy. Our model has a PA of 96.84%, MPA of 92.55%, MIoU of 86.05% and FWIoU of 94.22% on the test set, which is superior to other models.

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A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0988-4 ◽

2019 ◽

Vol 19 (S9) ◽

Cited By ~ 2

Author(s):

Vitoantonio Bevilacqua ◽

Antonio Brunetti ◽

Giacomo Donato Cascarano ◽

Andrea Guerriero ◽

Francesco Pesce ◽

...

Keyword(s):

Deep Learning ◽

Magnetic Resonance ◽

Kidney Disease ◽

Autosomal Dominant ◽

Automatic Segmentation ◽

Semantic Segmentation ◽

Magnetic Resonance Images ◽

Polycystic Kidney ◽

Autosomal Dominant Polycystic Kidney ◽

Segmentation Of Images

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

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Differentiation of Clear Cell Renal Cell Carcinoma from other Renal Cell Carcinoma Subtypes and Benign Oncocytoma Using Quantitative MDCT Enhancement Parameters

Medicina ◽

10.3390/medicina56110569 ◽

2020 ◽

Vol 56 (11) ◽

pp. 569

Author(s):

Claudia-Gabriela Moldovanu ◽

Bianca Petresc ◽

Andrei Lebovici ◽

Attila Tamas-Szora ◽

Mihai Suciu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Renal Tumors ◽

3D Analysis ◽

Imaging Features ◽

Enhancement Ratio ◽

Cell Renal Cell Carcinoma ◽

Renal Masses

Background and objectives: The use of non-invasive techniques to predict the histological type of renal masses can avoid a renal mass biopsy, thus being of great clinical interest. The aim of our study was to assess if quantitative multiphasic multidetector computed tomography (MDCT) enhancement patterns of renal masses (malignant and benign) may be useful to enable lesion differentiation by their enhancement characteristics. Materials and Methods: A total of 154 renal tumors were retrospectively analyzed with a four-phase MDCT protocol. We studied attenuation values using the values within the most avidly enhancing portion of the tumor (2D analysis) and within the whole tumor volume (3D analysis). A region of interest (ROI) was also placed in the adjacent uninvolved renal cortex to calculate the relative tumor enhancement ratio. Results: Significant differences were noted in enhancement and de-enhancement (diminution of attenuation measurements between the postcontrast phases) values by histology. The highest areas under the receiver operating characteristic curves (AUCs) of 0.976 (95% CI: 0.924–0.995) and 0.827 (95% CI: 0.752–0.887), respectively, were demonstrated between clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC)/oncocytoma. The 3D analysis allowed the differentiation of ccRCC from chromophobe RCC (chrRCC) with a AUC of 0.643 (95% CI: 0.555–0.724). Wash-out values proved useful only for discrimination between ccRCC and oncocytoma (43.34 vs 64.10, p < 0.001). However, the relative tumor enhancement ratio (corticomedullary (CM) and nephrographic phases) proved useful for discrimination between ccRCC, pRCC, and chrRCC, with the values from the CM phase having higher AUCs of 0.973 (95% CI: 0.929–0.993) and 0.799 (95% CI: 0.721–0.864), respectively. Conclusions: Our observations point out that imaging features may contribute to providing prognostic information helpful in the management strategy of renal masses.

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Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Sensors ◽

10.3390/s20082244 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2244

Author(s):

J. M. Jurado ◽

J. L. Cárdenas ◽

C. J. Ogayar ◽

L. Ortega ◽

F. R. Feito

Keyword(s):

High Resolution ◽

Point Cloud ◽

Near Infrared ◽

Automatic Segmentation ◽

Hierarchical Cluster ◽

Semantic Segmentation ◽

Heterogeneous Data ◽

Multispectral Images ◽

Three Dimensional Model ◽

Natural Materials

The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

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The Caris registry: Building a biomarker-focused database to advance patient care.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e21152 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e21152-e21152

Author(s):

Sheri Sanders ◽

Wendy Schroeder ◽

Alan Wright ◽

Jeff Field

Keyword(s):

Clinical Outcomes ◽

Clinical Decision Making ◽

Information Access ◽

Data Entry ◽

Clinical Decision ◽

Outcome Data ◽

Medical Community ◽

Tumor Biomarker ◽

Biomarker Analysis ◽

Biomarker Research

e21152 Background: The medical community is continually searching for the best way to treat cancer. The value and utility of biomarkers in guiding treatment decisions is widely accepted but remains a challenge for the bedside clinician and requires ongoing validation and correlation to clinical outcomes. Caris Life Sciences has a dedicated team of scientists who study volumes of scientific literature, synthesize biomarker research and by way of an evidence-based electronic rules engine, translates the application of the literature to biomarker analysis of tumor tissue (The Target Now Report) in support of biomarker-drug association evidence useful in clinical decision-making. Subsequently, Caris initiated the Caris Registry to capture clinical disease, treatment and outcome data from patients who have a Target Now Report. Methods: The Caris Registry is a web-based data entry platform based on an IRB approved protocol. The eligible subject for the Registry will have a qualified Target Now Report. All clinical data elements are defined and supported by the NCI caBIG standardized data dictionary. Disease history/status, treatments and outcomes are captured at enrollment with Day 1 defined as the date of the Target Now Report and every 9 months for 5 years or death whichever is first. Results: As of January 19, 2012, there are 68 participating centers across the country and 43 centers pending IRB submission. There are 852 Target Now cases enrolled with the following cancer lineage distribution: Breast 209, Ovary 169, Lung 117, Colon 79, Endometrium 33, and other 245. There are 323 completed follow up reports and 175 completed end of study reports capturing vital status and cancer related deaths. Conclusions: Caris has successfully launched a scientifically valid and regulatory compliant Registry and database intended to become a robust library of tumor biomarker results linked to clinical outcomes data. As the library grows, data mining could provide vital information access to researchers, pharmaceutical firms, government, academia and insurers for use in drug development, molecular and biomarker research, economic impact assessments, healthcare policy discussion and most importantly directing personalized cancer treatment.

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