Automatic Segmentation of the Kidneys and Cystic Renal Lesions on Non-Contrast CT Using a Convolutional Neural Network

• URL: http://arxiv.org/abs/2405.08282v1
• Date: Tue, 14 May 2024 02:34:56 GMT
• Title: Automatic Segmentation of the Kidneys and Cystic Renal Lesions on Non-Contrast CT Using a Convolutional Neural Network
• Authors: Lucas Aronson, Ruben Ngnitewe Massaa, Syed Jamal Safdar Gardezi, Andrew L. Wentland,
• Abstract summary: Prior automated segmentation models have largely ignored non-contrast computed tomography (CT) imaging. This work aims to implement and train a deep learning (DL) model to segment the kidneys and cystic renal lesions (CRLs) from non-contrast CT scans.
• Score: 0.1398098625978622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: Automated segmentation tools are useful for calculating kidney volumes rapidly and accurately. Furthermore, these tools have the power to facilitate large-scale image-based artificial intelligence projects by generating input labels, such as for image registration algorithms. Prior automated segmentation models have largely ignored non-contrast computed tomography (CT) imaging. This work aims to implement and train a deep learning (DL) model to segment the kidneys and cystic renal lesions (CRLs) from non-contrast CT scans. Methods: Manual segmentation of the kidneys and CRLs was performed on 150 non-contrast abdominal CT scans. The data were divided into an 80/20 train/test split and a deep learning (DL) model was trained to segment the kidneys and CRLs. Various scoring metrics were used to assess model performance, including the Dice Similarity Coefficient (DSC), Jaccard Index (JI), and absolute and percent error kidney volume and lesion volume. Bland-Altman (B-A) analysis was performed to compare manual versus DL-based kidney volumes. Results: The DL model achieved a median kidney DSC of 0.934, median CRL DSC of 0.711, and total median study DSC of 0.823. Average volume errors were 0.9% for renal parenchyma, 37.0% for CRLs, and 2.2% overall. B-A analysis demonstrated that DL-based volumes tended to be greater than manual volumes, with a mean bias of +3.0 ml (+/- 2 SD of +/- 50.2 ml). Conclusion: A deep learning model trained to segment kidneys and cystic renal lesions on non-contrast CT examinations was able to provide highly accurate segmentations, with a median kidney Dice Similarity Coefficient of 0.934. Keywords: deep learning; kidney segmentation; artificial intelligence; convolutional neural networks.

Related papers

• Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning [0.09999629695552192]
  Atherosclerosis, a chronic inflammatory disease affecting the large arteries, presents a global health risk. We employed a deep learning model to segment the vascular system in computed tomographic angiograms (CTAs) images. Our model achieved 83.4% average Dice accuracy in segmenting arteries from aorta to patella, advancing the state-of-the-art by 0.8%.
  arXiv  Detail & Related papers  (2023-11-27T16:47:09Z)
• Kidney and Kidney Tumour Segmentation in CT Images [0.0]
  This study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans. For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374.
  arXiv  Detail & Related papers  (2022-12-26T08:08:44Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• 3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate Between Papilledema and Optic Disc Drusen [44.754910718620295]
  We developed a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography ( OCT) scans. A classification algorithm was designed using 150 OCT volumes to perform 3-class classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen and prelamina swelling scores. Our AI approach accurately discriminated ODD from papilledema, using a single OCT scan.
  arXiv  Detail & Related papers  (2021-12-18T17:05:53Z)
• Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT [0.0]
  This paper assesses whether using clinical characteristics in addition to imaging can improve automated segmentation of kidney cancer. A total of 300 kidney cancer patients with contrast-enhanced CT scans and clinical characteristics were included. A cognizant sampling strategy was used to leverage clinical characteristics for improved segmentation.
  arXiv  Detail & Related papers  (2021-09-13T09:38:22Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans [3.8137985834223502]
  We develop and evaluate a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms. We assess the effect on accuracy of image resolution and training set size.
  arXiv  Detail & Related papers  (2020-09-18T00:12:33Z)
• Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT [2.9023633922848586]
  We implement and evaluate a multiple resolution residual network (MRRN) for multiple normal organs-at-risk (OAR) segmentation from computed tomography (CT) images. Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections. We trained our approach using 206 thoracic CT scans of lung cancer patients with 35 scans held out for validation to segment the left and right lungs, heart, esophagus, and spinal cord.
  arXiv  Detail & Related papers  (2020-05-27T22:39:09Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)
• Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [64.21642241351857]
  We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. We developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports. We also developed a model for multi-organ, multi-disease classification of chest CT volumes.
  arXiv  Detail & Related papers  (2020-02-12T00:59:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.