MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level

PAIP 2019: Liver cancer segmentation challenge

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation. (C) 2020 The Authors. Published by Elsevier B.V

Analyse du mouvement pour applications médicales et bio-médicales

L’analyse du mouvement, ou l’analyse d’une séquence d’images, est l’extension naturelle de l’analyse d’images à l’analyse de séries temporelles d’images. De nombreuses méthodes d’analyse de mouvement ont été développées dans le contexte de la vision par ordinateur, incluant le suivi de caractéristiques, le flot optique, l’analyse de points-clef, le recalage d’image, etc. Dans ce manuscrit, nous proposons une boite a outils de techniques d’analyse de mouvement adaptées à l’analyse de séquences biomédicales. Nous avons en particulier travaillé sur les cellules ciliées qui sont couvertes de cils qui battent. Elles sont présentes chez l’homme dans les zones nécessitant des mouvements de fluide. Dans les poumons et les voies respiratoires supérieures, les cils sont responsables de l’épuration muco-ciliaire, qui permet d’évacuer des poumons la poussière et autres impuretés inhalées. Les altérations de l’épuration mucociliaire peuvent être liées à des maladies touchant les cils, pouvant être génétiques ou acquises et peuvent être handicapantes. Ces maladies peuvent être caractérisées par l’analyse du mouvement des cils sous un microscope avec une résolution temporelle importante. Nous avons développé plusieurs outils et techniques pour réaliser ces analyses de manière automatiques et avec une haute précision, à la fois sur des biopsies et in-vivo. Nous avons aussi illustré nos techniques dans le contexte d’éco-toxicité en analysant le rythme cardiaque d’embryons de poissonsMotion analysis, or the analysis of image sequences, is a natural extension of image analysis to time series of images. Many methods for motion analysis have been developed in the context of computer vision, including feature tracking, optical flow, keypoint analysis, image registration, and so on. In this work, we propose a toolbox of motion analysis techniques suitable for biomedical image sequence analysis. We particularly study ciliated cells. These cells are covered with beating cilia. They are present in humans in areas where fluid motion is necessary. In the lungs and the upper respiratory tract, Cilia perform the clearance task, which means cleaning the lungs of dust and other airborne contaminants. Ciliated cells are subject to genetic or acquired diseases that can compromise clearance, and in turn cause problems in their hosts. These diseases can be characterized by studying the motion of cilia under a microscope and at high temporal resolution. We propose a number of novel tools and techniques to perform such analyses automatically and with high precision, both ex-vivo on biopsies, and in-vivo. We also illustrate our techniques in the context of eco-toxicity by analysing the beating pattern of the heart of fish embry

Brain MRI Segmentation using Fully Convolutional Network and Transfer Learning

International audienc

Segmentation des hyper-intensités de la matière blanche en quelques secondes à l'aide d'un réseau de neurones convolutif et de transfert d'apprentissage

National audienc

White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning

International audience<p>In this paper, we propose a fast automatic method that seg-ments white matter hyperintensities (WMH) in 3D brain MR images,using a fully convolutional network (FCN) and transfer learning. ThisFCN is VGG, pre-trained on ImageNet for natural image classification,and fine tuned with the training dataset of the MICCAI WMH Chal-lenge. We consider three images for each slice of volume to segment: thei-th T1 slice, the i-th FLAIR slice, and the residue of a morphologicaloperator that emphasizes small bright structures. These three 2D imagesare assembled to form a 2D color image, that inputs the FCN to obtainthe 2D segmentation of the i-th slice. We process all slices, and stack theresults to form the 3D output segmentation. With such a technique, thesegmentation of WMH on a 3D brain volume takes about 10 seconds. Ourtechnique was ranked 6-th over 20 participants at the MICCAI WMHChallenge.</p

<i>Where is VALDO? </i>:VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra-and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level