Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks

Shao, Muhan; Han, S.; Carass, Aaron; Li, Xiang; Blitz, Ari M.; Prince, Jerry L.; Ellingsen, Lotta M.

doi:10.1007/978-3-030-02628-8_9

Cited by 16 publications

(13 citation statements)

References 18 publications

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“…However, the influence of variability in acquisition protocols, pathology and image artefacts is an often overlooked problem. In a recent work, Shao et al [26] demonstrated the shortcomings of deep learning in the presence of pathology for brain MR segmentation with an emphasis on the selection of training data.…”

Section: End-to-end Techniquesmentioning

confidence: 99%

Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space

Öksüz

Clough

Ruijsink

et al. 2019

Lecture Notes in Computer Science

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Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We quantitatively compare our method with a variety of techniques for jointly recovering image quality and performing image segmentation. We showcase better performance compared to state-of-the-art image correction techniques. Moreover, our method preserves the quality of uncorrupted images and therefore can be utilised as a global image reconstruction algorithm.

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Section: End-to-end Techniquesmentioning

confidence: 99%

Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space

Öksüz

Clough

Ruijsink

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Furthermore, the network combined the feature maps created at different resolution levels to refine the final classification. A preliminary version of this work was reported in conference form ( Shao et al, 2018b ); here we present an improvement of this work with more extensive validation and comparisons.…”

Section: Introductionmentioning

confidence: 99%

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Shao

Han

Carass

et al. 2019

NeuroImage: Clinical

Self Cite

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Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly ( p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system.

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“…Deep learning (DL) has been applied with success in proofs of concept across biomedical imaging modalities and medical specialties [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . DL models can classify images by disease or structure and can segment, track, and measure structures within images.…”

Section: Introductionmentioning

confidence: 99%

ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging

Chinn

Arora

Arnaout

et al. 2021

Preprint

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Deep learning (DL) requires labeled data. Labeling medical images requires medical expertise, which is often a bottleneck. It is therefore useful to prioritize labeling those images that are most likely to improve a model's performance, a practice known as instance selection. Here we introduce ENRICH, a method that selects images for labeling based on how much novelty each image adds to the growing training set. In our implementation, we use cosine similarity between autoencoder embeddings to measure that novelty. We show that ENRICH achieves nearly maximal performance on classification and segmentation tasks using only a fraction of available images, and outperforms the default practice of selecting images at random. We also present evidence that instance selection may perform categorically better on medical vs. non-medical imaging tasks. In conclusion, ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling for DL.

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Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks

Cited by 16 publications

References 18 publications

Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space

Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging

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