Deep similarity learning for multimodal medical images

Cheng, Xi; Zhang, Li; Zheng, Yefeng

doi:10.1080/21681163.2015.1135299

Cited by 130 publications

(88 citation statements)

References 2 publications

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“…Wu et al (2013), Simonovsky et al (2016), and Cheng et al (2015) used the first strategy to try to optimize registration algorithms. Cheng et al (2015) used two types of stacked auto-encoders to assess the local similarity between CT and MRI images of the head. Both auto-encoders take vectorized image patches of CT and MRI and reconstruct them through four layers.…”

Section: Registrationmentioning

confidence: 99%

“…Broadly speaking, two strategies are prevalent in current literature: (1) using deep-learning networks to estimate a similarity measure for two images to drive an iterative optimization strategy, and (2) to directly predict transformation parameters using deep regression networks. Wu et al (2013), Simonovsky et al (2016), and Cheng et al (2015) used the first strategy to try to optimize registration algorithms. Cheng et al (2015) used two types of stacked auto-encoders to assess the local similarity between CT and MRI images of the head.…”

Section: Registrationmentioning

confidence: 99%

See 1 more Smart Citation

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,639

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: Registrationmentioning

confidence: 99%

Section: Registrationmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,639

5,414

View full text Add to dashboard Cite

show abstract

“…[24] is a current result in semantic segmentation. Quite a few [25,72,73,94] have utilized deep learning to perform a multimodal registration. Deep learning is also used for the separation of staining colors [3,3,20,39,93] or for registration [10,27,28].…”

Section: Deep Learningmentioning

confidence: 99%

The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections

Lobachev

2019

Preprint

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This work presents two methods that facilitate a 3D reconstruction of microscopic blood vessels in the volume slightly larger than 1 mm 3 . The source of the data are histological serial sections, i. e., microscopic images of probes, stained with immunohistochemistry. Odd and even sections have different stainings in our primary data set. Thus, firstly, an approach to register an alternately-stained series is presented. With image filtering and a feature-detection-based registration we obtain a registered stack of 148 serial sections. The series has missing sections, locally damaged sections, artifacts from acquisition. All these hinder correct connectivity of blood vessels. With our second approach we interpolate the missing information while maintaining the connectivity. We achieve this with deformations based on dense optical flow. The presented methodology is applicable to further histological series. A combination of both approaches allows us to reconstruct more than 76 % larger volumes. An important detail was the composition mode of images. Summarizing, we use methods from image processing and computer vision to create large-scale 3D models from immunostained histological serial sections.

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“…One is using deep learning to estimate the similarity metric, which is then used to drive an iterative optimization strategy, as seen in Cheng et al and Simonovsky et al [30,31]. In the study by Simonovosky et al, the problem is designed as a classification task, where a CNN is set to discriminate between alignment and misalignment of the two superimposed MRI brain images (T1 and T2 weighting of neonatal brains) [31].…”

Section: Image Registrationmentioning

confidence: 99%

“…In the study by Simonovosky et al, the problem is designed as a classification task, where a CNN is set to discriminate between alignment and misalignment of the two superimposed MRI brain images (T1 and T2 weighting of neonatal brains) [31]. The study of Cheng et al is similar to this method in many ways, however, they used an autoencoder to pre-train the network [30].…”

Section: Image Registrationmentioning

confidence: 99%