Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities

Moeskops, Pim; Wolterink, Jelmer M.; Velden, Bas H. M. van der; Gilhuijs, Kenneth G. A.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

doi:10.1007/978-3-319-46723-8_55

Cited by 257 publications

(177 citation statements)

References 13 publications

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“…fCNNs have also been extended to 3D and have been applied to multiple targets at once: Korez et al (2016), used 3D fCNNs to generate vertebral body likelihood maps which drove deformable models for vertebral body segmentation in MR images, Zhou et al (2016) segmented nineteen targets in the human torso, and Moeskops et al (2016b) trained a single fCNN to segment brain MRI, the pectoral muscle in breast MRI, and the coronary arteries in cardiac CT angiography (CTA).…”

Section: Organ and Substructure Segmentationmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

10,074

5,414

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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. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Section: Organ and Substructure Segmentationmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

10,074

5,414

View full text Add to dashboard Cite

show abstract

“…Other architectures, such as the U-net 54 , have been specifically designed for medical images. Studies have reported that a single deep learning system is able to perform diverse segmentation tasks across multiple modalities and tissue types, including brain MRI, breast MRI and cardiac CT angiography (CTA), without task-specific training 55 . Others describe deep learning methods for brain MRI segmentation that completely eliminate the need for image registration, a required preprocessing step in atlas-based methods 56 .…”

Section: Impact On Oncology Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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“…Medical image segmentation has been studied for decades but remains a challenging problem today . Since the invention of the convolutional neural network (CNN), there have been many attempts to utilize CNNs for various image segmentation tasks . Most of the early methods used a simple “sliding window” approach which has many drawbacks including huge overlap of image patches and repeated convolution for the same pixel .…”

Section: Introductionmentioning

confidence: 99%

A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy

Mazur

et al. 2018

Medical Physics

126

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Purpose The purpose of this study was to expedite the contouring process for MRI‐guided adaptive radiotherapy (MR‐IGART), a convolutional neural network (CNN) deep‐learning (DL) model is proposed to accurately segment the liver, kidneys, stomach, bowel and duodenum in 3D MR images. Methods Images and structure contours for 120 patients were collected retrospectively. Treatment sites included pancreas, liver, stomach, adrenal gland, and prostate. The proposed DL model contains a voxel‐wise label prediction CNN and a correction network which consists of two sub‐networks. The prediction CNN and sub‐networks in the correction network each includes a dense block which consists of twelve densely connected convolutional layers. The correction network was designed to improve the voxel‐wise labeling accuracy of a CNN by learning and enforcing implicit anatomical constraints in the segmentation process. Its sub‐networks learn to fix the erroneous classification of its previous network by taking as input both the original images and the softmax probability maps generated from its previous sub‐network. The parameters of each sub‐network were trained independently using piecewise training. The model was trained on 100 datasets, validated on 10 datasets and tested on the remaining 10 datasets. Dice coefficient, Hausdorff distance (HD) were calculated to evaluate the segmentation accuracy. Results The proposed DL model was able to segment the organs with good accuracy. The correction network outperformed the conditional random field (CRF), a most comparable method that is usually applied as a post‐processing step. For the 10 testing patients, the average Dice coefficients were 95.3 ± 0.73, 93.1 ± 2.22, 85.0 ± 3.75, 86.6 ± 2.69, and 65.5 ± 8.90 for liver, kidneys, stomach, bowel, and duodenum, respectively. The mean Hausdorff Distance (HD) were 5.41 ± 2.34, 6.23 ± 4.59, 6.88 ± 4.89, 5.90 ± 4.05, and 7.99 ± 6.84 mm, respectively. Manual contouring, as to correct the automatic segmentation results, was four times as fast as manual contouring from scratch. Conclusion The proposed method can automatically segment the liver, kidneys, stomach, bowel, and duodenum in 3D MR images with good accuracy. It is useful to expedite the manual contouring for MR‐IGART.

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Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities

Cited by 257 publications

References 13 publications

A survey on deep learning in medical image analysis

A survey on deep learning in medical image analysis

Artificial intelligence in radiology

A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy

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