Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Deep neural networks have increased the accuracy of automatic segmentation, however their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function. Our theory also allows us to define the leaf-Dice loss, a label-set generalisation of the Dice loss particularly suited for partial supervision with only missing labels. Using the leaf-Dice loss, we set a new state of the art in partially supervised learning for fetal brain 3D MRI segmentation. We achieve a deep neural network able to segment white matter, ventricles, cerebellum, extra-ventricular CSF, cortical gray matter, deep gray matter, brainstem, and corpus callosum based on fetal brain 3D MRI of anatomically normal fetuses or with open spina bifida. Our implementation of the proposed label-set loss functions is available at https://github.com/LucasFidon/label-set-loss-functions

Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Deep neural networks have increased the accuracy of automatic segmentation, however their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function. Our theory also allows us to define the leaf-Dice loss, a label-set generalisation of the Dice loss particularly suited for partial supervision with only missing labels. Using the leaf-Dice loss, we set a new state of the art in partially supervised learning for fetal brain 3D MRI segmentation. We achieve a deep neural network able to segment white matter, ventricles, cerebellum, extra-ventricular CSF, cortical gray matter, deep gray matter, brainstem, and corpus callosum based on fetal brain 3D MRI of anatomically normal fetuses or with open spina bifida. Our implementation of the proposed label-set loss functions is available at https://github.com/LucasFidon/label-set-loss-functions

Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI

The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability of the developing brain anatomy compared to non-pathological cases. A small number of abnormal cases, as is typically available in clinical datasets used for training, are unlikely to fairly represent the rich variability of abnormal developing brains. This leads machine learning systems trained by maximizing the average performance to be biased toward non-pathological cases. This problem was recently referred to as hidden stratification. To be suited for clinical use, automatic segmentation methods need to reliably achieve high-quality segmentation outcomes also for pathological cases. In this paper, we show that the state-of-the-art deep learning pipeline nnU-Net has difficulties to generalize to unseen abnormal cases. To mitigate this problem, we propose to train a deep neural network to minimize a percentile of the distribution of per-volume loss over the dataset. We show that this can be achieved by using Distributionally Robust Optimization (DRO). DRO automatically reweights the training samples with lower performance, encouraging nnU-Net to perform more consistently on all cases. We validated our approach using a dataset of 368 fetal brain T2w MRIs, including 124 MRIs of open spina bifida cases and 51 MRIs of cases with other severe abnormalities of brain development

ADPedKD: A Global Online Platform on the Management of Children With ADPKD

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of renal failure. For several decades, ADPKD was regarded as an adult-onset disease. In the past decade, it has become more widely appreciated that the disease course begins in childhood. However, evidence-based guidelines on how to manage and approach children diagnosed with or at risk of ADPKD are lacking. Also, scoring systems to stratify patients into risk categories have been established only for adults. Overall, there are insufficient data on the clinical course during childhood. We therefore initiated the global ADPedKD project to establish a large international pediatric ADPKD cohort for deep characterization. Methods: Global ADPedKD is an international multicenter observational study focusing on childhooddiagnosed ADPKD. This collaborative project is based on interoperable Web-based databases, comprising 7 regional and independent but uniformly organized chapters, namely Africa, Asia, Australia, Europe, North America, South America, and the United Kingdom. In the database, a detailed basic data questionnaire, including genetics, is used in combination with data entry from follow-up visits, to provide both retrospective and prospective longitudinal data on clinical, radiologic, and laboratory findings, as well as therapeutic interventions. Discussion: The global ADPedKD initiative aims to characterize in detail the most extensive international pediatric ADPKD cohort reported to date, providing evidence for the development of unified diagnostic, follow-up, and treatment recommendations regarding modifiable disease factors. Moreover, this registry will serve as a platform for the development of clinical and/or biochemical markers predicting the risk of early and progressive disease

ADPedKD: A Global Online Platform on the Management of Children With ADPKD

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of renal failure. For several decades, ADPKD was regarded as an adult-onset disease. In the past decade, it has become more widely appreciated that the disease course begins in childhood. However, evidence-based guidelines on how to manage and approach children diagnosed with or at risk of ADPKD are lacking. Also, scoring systems to stratify patients into risk categories have been established only for adults. Overall, there are insufficient data on the clinical course during childhood. We therefore initiated the global ADPedKD project to establish a large international pediatric ADPKD cohort for deep characterization. Methods: Global ADPedKD is an international multicenter observational study focusing on childhood-diagnosed ADPKD. This collaborative project is based on interoperable Web-based databases, comprising 7 regional and independent but uniformly organized chapters, namely Africa, Asia, Australia, Europe, North America, South America, and the United Kingdom. In the database, a detailed basic data questionnaire, including genetics, is used in combination with data entry from follow-up visits, to provide both retrospective and prospective longitudinal data on clinical, radiologic, and laboratory findings, as well as therapeutic interventions. Discussion: The global ADPedKD initiative aims to characterize in detail the most extensive international pediatric ADPKD cohort reported to date, providing evidence for the development of unified diagnostic, follow-up, and treatment recommendations regarding modifiable disease factors. Moreover, this registry will serve as a platform for the development of clinical and/or biochemical markers predicting the risk of early and progressive disease

ADPedKD: A Global Online Platform on the Management of Children With ADPKD

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of renal failure. For several decades, ADPKD was regarded as an adult-onset disease. In the past decade, it has become more widely appreciated that the disease course begins in childhood. However, evidence-based guidelines on how to manage and approach children diagnosed with or at risk of ADPKD are lacking. Also, scoring systems to stratify patients into risk categories have been established only for adults. Overall, there are insufficient data on the clinical course during childhood. We therefore initiated the global ADPedKD project to establish a large international pediatric ADPKD cohort for deep characterization. Methods: Global ADPedKD is an international multicenter observational study focusing on childhood-diagnosed ADPKD. This collaborative project is based on interoperable Web-based databases, comprising 7 regional and independent but uniformly organized chapters, namely Africa, Asia, Australia, Europe, North America, South America, and the United Kingdom. In the database, a detailed basic data questionnaire, including genetics, is used in combination with data entry from follow-up visits, to provide both retrospective and prospective longitudinal data on clinical, radiologic, and laboratory findings, as well as therapeutic interventions. Discussion: The global ADPedKD initiative aims to characterize in detail the most extensive international pediatric ADPKD cohort reported to date, providing evidence for the development of unified diagnostic, follow-up, and treatment recommendations regarding modifiable disease factors. Moreover, this registry will serve as a platform for the development of clinical and/or biochemical markers predicting the risk of early and progressive disease.status: publishe

ADPedKD: A Global Online Platform on the Management of Children With ADPKD

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of renal failure. For several decades, ADPKD was regarded as an adult-onset disease. In the past decade, it has become more widely appreciated that the disease course begins in childhood. However, evidence-based guidelines on how to manage and approach children diagnosed with or at risk of ADPKD are lacking. Also, scoring systems to stratify patients into risk categories have been established only for adults. Overall, there are insufficient data on the clinical course during childhood. We therefore initiated the global ADPedKD project to establish a large international pediatric ADPKD cohort for deep characterization. Methods: Global ADPedKD is an international multicenter observational study focusing on childhood-diagnosed ADPKD. This collaborative project is based on interoperable Web-based databases, comprising 7 regional and independent but uniformly organized chapters, namely Africa, Asia, Australia, Europe, North America, South America, and the United Kingdom. In the database, a detailed basic data questionnaire, including genetics, is used in combination with data entry from follow-up visits, to provide both retrospective and prospective longitudinal data on clinical, radiologic, and laboratory findings, as well as therapeutic interventions. Discussion: The global ADPedKD initiative aims to characterize in detail the most extensive international pediatric ADPKD cohort reported to date, providing evidence for the development of unified diagnostic, follow-up, and treatment recommendations regarding modifiable disease factors. Moreover, this registry will serve as a platform for the development of clinical and/or biochemical markers predicting the risk of early and progressive disease