Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

Andermatt, Simon; Pezold, Simon; Cattin, Philippe C.

doi:10.1007/978-3-319-46976-8_15

Cited by 82 publications

(82 citation statements)

References 5 publications

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“…Teams hadi, lrde, misp, neuro.ml, nih cidi, nist, skkumedneuro, text class, and upc dlmi have lAVD values above 3.0. For full details, see Appendix C Figures 27,14,13,25,15,24,19,26,and 23,respectively. the sixty training images, without a consensus reading, to determine inter-observer agreement. The top-ranking methods achieve similar or superior performance as these two individual observers, which suggests that automatic methods might be able to replace individual observers in WMH segmentation.…”

Section: Discussionmentioning

confidence: 99%

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

Kuijf

Casamitjana

Collins

et al. 2019

IEEE Trans. Med. Imaging

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220

212

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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. 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 method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. 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. Additionally, methods were ranked on their inter-scanner robustness.Twenty participants submitted their method 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 methods generalize to unseen scanners.The challenge remains open for future submissions and provides a public platform for method evaluation.

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Section: Discussionmentioning

confidence: 99%

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

Kuijf

Casamitjana

Collins

et al. 2019

IEEE Trans. Med. Imaging

Self Cite

220

212

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“…The majority of the state-of-the-art methods based on deep learning exploit multi-modal MRI data [27,28,29,30]. Yet, in real-case scenarios and due to time constraints, the acquisition of different MRI sequences for anatomical analysis is rarely done: in most studies a single sequence is used -with T1 w being the most popular protocol.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi

Benini

Signoroni

et al. 2020

Medical Image Analysis

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Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This process entails a loss of global contextual information, thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed of 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only a few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements favour our solution with respect to widely adopted atlas-based software.

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“…Yet another interesting class of segmentation algorithms is the use of recurrent networks for medical image segmentation. Poudel et al demonstrate this for a recurrent fully convolutional neural network on multi-slice MRI cardiac data [79], while Andermatt et al show effectiveness of GRUs for brain segmentation [80].…”

Section: Image Segmentationmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

422

224

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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

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Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

Cited by 82 publications

References 5 publications

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

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

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

A gentle introduction to deep learning in medical image processing

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