Role of deep learning in infant brain MRI analysis

Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them

Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.Peer ReviewedPostprint (published version

Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation

Medical image segmentation of tumors and organs at risk is a time-consuming yet critical process in the clinic that utilizes multi-modality imaging (e.g, different acquisitions, data types, and sequences) to increase segmentation precision. In this paper, we propose a novel framework, Modality-Agnostic learning through Multi-modality Self-dist-illation (MAG-MS), to investigate the impact of input modalities on medical image segmentation. MAG-MS distills knowledge from the fusion of multiple modalities and applies it to enhance representation learning for individual modalities. Thus, it provides a versatile and efficient approach to handle limited modalities during testing. Our extensive experiments on benchmark datasets demonstrate the high efficiency of MAG-MS and its superior segmentation performance than current state-of-the-art methods. Furthermore, using MAG-MS, we provide valuable insight and guidance on selecting input modalities for medical image segmentation tasks

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Unpaired multi-modal segmentation via knowledge distillation

Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D U-net, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms single-modal training and previous multi-modal approaches

Learning from Complex Neuroimaging Datasets

Advancements in Magnetic Resonance Imaging (MRI) allowed for the early diagnosis of neurodevelopmental disorders and neurodegenerative diseases. Neuroanatomical abnormalities in the cerebral cortex are often investigated by examining group-level differences of brain morphometric measures extracted from highly-sampled cortical surfaces. However, group-level differences do not allow for individual-level outcome prediction critical for the application to clinical practice. Despite the success of MRI-based deep learning frameworks, critical issues have been identified: (1) extracting accurate and reliable local features from the cortical surface, (2) determining a parsimonious subset of cortical features for correct disease diagnosis, (3) learning directly from a non-Euclidean high-dimensional feature space, (4) improving the robustness of multi-task multi-modal models, and (5) identifying anomalies in imbalanced and heterogeneous settings. This dissertation describes novel methodological contributions to tackle the challenges above. First, I introduce a Laplacian-based method for quantifying local Extra-Axial Cerebrospinal Fluid (EA-CSF) from structural MRI. Next, I describe a deep learning approach for combining local EA-CSF with other morphometric cortical measures for early disease detection. Then, I propose a data-driven approach for extending convolutional learning to non-Euclidean manifolds such as cortical surfaces. I also present a unified framework for robust multi-task learning from imaging and non-imaging information. Finally, I propose a semi-supervised generative approach for the detection of samples from untrained classes in imbalanced and heterogeneous developmental datasets. The proposed methodological contributions are evaluated by applying them to the early detection of Autism Spectrum Disorder (ASD) in the first year of the infant’s life. Also, the aging human brain is examined in the context of studying different stages of Alzheimer’s Disease (AD).Doctor of Philosoph