Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Deep Learning (DL) based methods outperform non-DL methods in terms of accuracy but lack explainability. Despite this trade-off, when it comes to making high-stakes decisions, it's important to prioritize understandable Computer-Aided Diagnosis (CADx) that suggests a course of action based on reasonable evidence, rather than a model prescribing a course of action. In this proposal, we learn Prototypical Parts (PPs) per kidney stone subtype, which are used by the DL model to generate an output classification. Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable. In addition, we modify global visual characteristics to describe their relevance to the PPs and the sensitivity of our model's performance. With this, we provide explanations with additional information at the sample, class and model levels in contrast to previous works. Although our implementation's average accuracy is lower than state-of-the-art (SOTA) non-interpretable DL models by 1.5 %, our models perform 2.8% better on perturbed images with a lower standard deviation, without adversarial training. Thus, Learning PPs has the potential to create more robust DL models.
Southern Illinois University Carbondale（南伊利诺伊大学卡本代尔分校）
In this paper, we investigated a C-arm tomographic technique as a new three dimensional (3D) kidney imaging method for nephrolithiasis and kidney stone detection over view angle less than 180o. Our C-arm tomographic technique provides a series of two dimensional (2D) images with a single scan over 40o view angle. Experimental studies were performed with a kidney phantom that was formed from a pig kidney with two embedded kidney stones. Different reconstruction methods were developed for C-arm tomographic technique to generate 3D kidney information including: point by point back projection (BP)…
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features. Our approach was specifically designed to mimic the morpho-constitutional analysis used by urologists to visually classify kidney stones by inspecting the sections and surfaces of their fragments. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 10\% in terms of precision of the kidney stones classification.
Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition
CINVESTAV（墨西哥国立理工研究中心）；Universidad Nacional de Ingenieria（国立工程大学）；Tecnologico de Monterrey（蒙特雷科技大学）；CRAN UMR 7039（CRAN研究中心）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）
USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification
Shenzhen Research Institute of Big Data（深圳市大数据研究院）；Zhejiang University of Finance and Economics（浙江财经大学）；Anhui University of Finance and Economics（安徽财经大学）；The Second Affiliated Hospital of Chinese University of Hong Kong (Longgang District People’s Hospital of Shenzhen)（香港中文大学第二附属医院（深圳市龙岗区人民医院））
Tecnológico de Monterrey（蒙特雷理工学院）；CHU Nancy（南锡大学中心医院）；CHU Pellegrin（佩勒格兰大学中心医院）；Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional（国立理工学院高级研究中心）；Université de Lorraine（洛林大学）
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as it has been repeatedly demonstrated, deep learning methods trained on a dataset do not generalize well to datasets from other domains or even to similar datasets, due to data distribution shifts. In this work, we propose the use of a meta-learning based few-shot learning approach to alleviate these problems. In order to demonstrate its efficacy, we use two datasets of kidney stones samples acquired with different end…
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
Tecnologico de Monterrey（蒙特雷科技大学）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；CINVESTAV（高级研究中心）；CHU Nancy（南希大学医院中心）；Hôpital Tenon（tenon医院）；AP-HP（巴黎公共医疗集团）
University of Rochester（罗切斯特大学）；University of Rochester Medical Center（罗切斯特大学医学中心）
Recently, deep learning has produced encouraging results for kidney stone classification using endoscope images. However, the shortage of annotated training data poses a severe problem in improving the performance and generalization ability of the trained model. It is thus crucial to fully exploit the limited data at hand. In this paper, we propose SegPrompt to alleviate the data shortage problems by exploiting segmentation maps from two aspects. First, SegPrompt integrates segmentation maps to facilitate classification training so that the classification model is aware of the regions of inter…
Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
Tecnologico de Monterrey（蒙特雷理工学院）；CRAN (UMR 7039, Université de Lorraine and CNRS)（CRAN研究中心（洛林大学与法国国家科学研究中心联合实验室））；CINVESTAV（高级研究中心）；NGERE - Nutrition-Génétique et Exposition aux Risques Environnementaux（营养、遗传与环境风险暴露研究组）；CHRU Nancy（南锡大学医院中心）；Hôpital Tenon, AP-HP（tenon医院，巴黎公共医疗集团）
Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition
Tecnologico de Monterrey（蒙特雷理工学院）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；CHRU de Nancy（南锡大学医院中心）；Centro de Investigación y de Estudios Avanzados（高级研究中心）
Evaluation of Few-Shot Learning Methods for Kidney Stone Type Recognition in Ureteroscopy
Tecnologico de Monterrey, School of Engineering and Sciences（墨西哥技术学院，工程与科学学院）；Université de Lorraine, CNRS, CRAN (UMR 7039)（洛林大学，法国国家科学研究中心，CRAN（UMR 7039））；CHRU de Nancy-Brabois, service d’urologie（南布列塔尼大学医院，泌尿科）
INAOE（国家天体物理、光学与电子学研究所）；UAG（瓜达拉哈拉自治大学）；Tecnologico de Monterrey（蒙特雷科技大学）；Viettel Cyberspace Center（越南军用电子电信网络空间中心）；CHU Nancy（南锡大学医院）；IADI-UL-INSERM (U1254)（影像引导自适应诊断与介入实验室-洛林大学-法国国家健康与医学研究院（U1254））；CHU Pellegrin（佩勒格兰大学医院）；CRAN (Université de Lorraine and CNRS)（洛林大学与法国国家科学研究中心自动控制研究中心）
Knowing the type (i.e., the biochemical composition) of kidney stones is crucial to prevent relapses with an appropriate treatment. During ureteroscopies, kidney stones are fragmented, extracted from the urinary tract, and their composition is determined using a morpho-constitutional analysis. This procedure is time consuming (the morpho-constitutional analysis results are only available after some days) and tedious (the fragment extraction lasts up to an hour). Identifying the kidney stone type only with the in-vivo endoscopic images would allow for the dusting of the fragments, while the mor…
On the in vivo recognition of kidney stones using machine learning
Tecnologico de Monterrey（蒙特雷理工学院）；Université de Lorraine（洛林大学）；CHRU Pellegrin（佩勒兰大学地区医疗中心）；IADI-UL-INSERM（洛林大学-法国国家健康与医学研究院先进诊断与工业应用研究所）；CHRU Nancy（南锡大学地区医疗中心）
Tecnologico de Monterrey（蒙特雷理工学院）；CHU Nancy（南锡大学中心医院）；Université de Lorraine（洛林大学）
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses. However, currently, the associated ex-vivo diagnosis (known as morpho-constitutional analysis, MCA) is time-consuming, expensive, and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Shallow methods have been demonstrated to be reliable and interpretable…
University of Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；Bordeaux INP（波尔多国立理工学院）
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…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process …
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors
School of Engineering & Technology, BML Munjal University（BML Munjal大学工程与技术学院）；Center for Advanced Data and Computational Science, BML Munjal University（BML Munjal大学先进数据与计算科学中心）
A metric learning approach for endoscopic kidney stone identification
Centro de Investigación y de Estudios Avanzados（高级研究中心）；Tecnologico de Monterrey（蒙特雷科技大学）；Centre de Recherche en Automatique de Nancy（南锡自动化研究中心）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）；CHU Nancy（南锡大学附属医院）
University of Oxford（牛津大学）；Oxford University Hospitals NHS Trust（牛津大学医院NHS信托基金会）
Kidney stones represent a considerable burden for public health-care systems. Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment of kidney stones. Automated segmentation of kidney stones and laser fiber is an important initial step to performing any automated quantitative analysis of the stones, particularly stone-size estimation, that helps the surgeon decide if the stone requires more fragmentation. Factors such as turbid fluid inside the cavity, specularities, motion blur due to kidney movements and camera motion, bleeding, and stone debris…
Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
Tecnologico de Monterrey, School of Engineering and Sciences, Mexico（墨西哥蒙特雷科技大学工程与科学学院）；CRAN UMR 7039, Université de Lorraine and CNRS, Nancy, France（法国洛林大学CNRS CRAN UMR 7039研究中心）；CINVESTAV, Guadalajara, Mexico（墨西哥瓜达拉哈拉科学技术研究所）；CHRU Nancy, Service d’urologie de Brabois, Nancy, France（法国南锡布拉布瓦大学医院泌尿外科）
Assessing the generalization performance of SAM for ureteroscopy scene understanding
Tecnologico de Monterrey, School of Engineering and Sciences（墨西哥技术学院，工程与科学学院）；CRAN UMR 7039, Université de Lorraine and CNRS（洛林大学CRAN UMR 7039及CNRS）；CHRU de Nancy-Brabois, service d’urologie（南布拉欧斯医院泌尿科）
