Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos

• URL: http://arxiv.org/abs/2205.06093v1
• Date: Thu, 12 May 2022 13:58:57 GMT
• Title: Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos
• Authors: Vincent Estrade, Michel Daudon, Emmanuel Richard, Jean-Christophe Bernhard, Franck Bladou, Gregoire Robert, Laurent Facq, Baudouin Denis de Senneville
• Abstract summary: LASER-based fragmentation of urinary stones, which is now the most established chirurgical intervention, may destroy the morphology of the targeted stone. In the current study, we assess the performance and added value of processing complete digital endoscopic video sequences for the automatic recognition of stone morphological features.
• Score: 0.30382867467444796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The collection and the analysis of kidney stone morphological criteria are essential for an aetiological diagnosis of stone disease. However, in-situ LASER-based fragmentation of urinary stones, which is now the most established chirurgical intervention, may destroy the morphology of the targeted stone. In the current study, we assess the performance and added value of processing complete digital endoscopic video sequences for the automatic recognition of stone morphological features during a standard-of-care intra-operative session. To this end, a computer-aided video classifier was developed to predict in-situ the morphology of stone using an intra-operative digital endoscopic video acquired in a clinical setting. The proposed technique was evaluated on pure (i.e. include one morphology) and mixed (i.e. include at least two morphologies) stones involving "Ia/Calcium Oxalate Monohydrate (COM)", "IIb/ Calcium Oxalate Dihydrate (COD)" and "IIIb/Uric Acid (UA)" morphologies. 71 digital endoscopic videos (50 exhibited only one morphological type and 21 displayed two) were analyzed using the proposed video classifier (56840 frames processed in total). Using the proposed approach, diagnostic performances (averaged over both pure and mixed stone types) were as follows: balanced accuracy=88%, sensitivity=80%, specificity=95%, precision=78% and F1-score=78%. The obtained results demonstrate that AI applied on digital endoscopic video sequences is a promising tool for collecting morphological information during the time-course of the stone fragmentation process without resorting to any human intervention for stone delineation or selection of good quality steady frames. To this end, irrelevant image information must be removed from the prediction process at both frame and pixel levels, which is now feasible thanks to the use of AI-dedicated networks.

Related papers

• Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning [0.8431877864777444]
  The proposed approach transfers knowledge learned on a set of images of kidney stones acquired with a CCD camera to a final model that classifies images from endoscopic images. The results show that learning features from different domains with similar information helps to improve the performance of a model that performs classification in real conditions.
  arXiv  Detail & Related papers  (2022-10-24T23:22:22Z)
• 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)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Images [0.5709573160862248]
  A deep convolutional neural network was trained to predict the composition of both pure and mixed stones. A highest sensitivity of 98 % was obtained for the type "pure IIIb/UA" using surface images. Of the mixed type "Ia/COM+IIb/COD", Ia/COM was predicted in 84 % of cases using surface images, IIb/COD in 70 % of cases, and both in 65 % of cases.
  arXiv  Detail & Related papers  (2021-05-22T10:52:19Z)
• Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy [0.7874708385247353]
  We propose an end-to-end CNN-based framework for the segmentation of stones and laser fiber. We show that our proposed model generalizes better on a new clinical dataset showing a mean improvement of 25.4%, 20%, and 11% over UNet, HybResUNet, and DeepLabv3+, respectively.
  arXiv  Detail & Related papers  (2021-04-02T22:47:21Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Assessing deep learning methods for the identification of kidney stones in endoscopic images [0.0]
  Knowing the type (i.e., the biochemical composition) of kidney stones is crucial to prevent relapses. During ureteroscopies, kidney stones are fragmented, extracted from the urinary tract, and their composition is determined using a morpho-constitutional analysis. This paper discusses and compares five classification methods including deep convolutional neural networks (DCNN)-based approaches and traditional (non DCNN-based) ones.
  arXiv  Detail & Related papers  (2021-03-01T17:31:01Z)
• StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification [6.690876060631452]
  We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath. Each image was generated in 1.84 + 0.03 seconds using a single GTX V TITAN and pytorch. Our results imply that our style-transfer augmentation technique improves histological classification performance.
  arXiv  Detail & Related papers  (2020-07-09T18:02:49Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.