Learning-Based Affine Registration of Histological Images

Wodziński, Marek; Müller, Henning

doi:10.1007/978-3-030-50120-4_2

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…The deep learning model is based on the work of Wodzinski et al, 32 with the following two major improvements. First, some of the naive convolutional layers are substituted with dilated convolution kernels so as to enlarge the receptive field.…”

Section: Methodsmentioning

confidence: 99%

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

He,

Wang,

Zeng

et al. 2023

Int J Imaging Syst Tech

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Histopathological diagnosis is the golden standard for cancer diagnosis and grading. The past decade has witnessed great successes of vision‐based deep learning in computer‐aided cancer diagnosis. However, fully‐supervised methods require collecting large amounts of annotated data, which is a major challenge. In this research, we propose RMIL, a novel registration‐enhanced multiple instance learning pipeline, for cervical cancer biopsy grading of whole slide images as well as patch‐level regions. It requires only slide‐level annotations and a small amount of patch‐level annotations for training. To enhance it, an efficient registration method is implemented to generate more labeled patches with discriminative local features as data augmentations. In addition, attention mechanisms and self‐supervised learning are introduced to enhance feature extraction. Experiments showed that its classification accuracy is increased by 11.11%, compared to the vanilla multiple instance learning methods. The method has great potential for transferring to other scenarios.

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Section: Methodsmentioning

confidence: 99%

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

He,

Wang,

Zeng

et al. 2023

Int J Imaging Syst Tech

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show abstract

“…Second, we address the problem of ground truth generation. In the absence of ground truth data generated by human experts, which may require considerable time commitments from them, researchers have used alternative methods such as using a validated algorithm 38 , 39 . Here, we introduce a method of increasing the accuracy of such validated algorithms when generating ground truth data for multi-modal medical image registration.…”

Section: Proposed Methodsmentioning

confidence: 99%

A deep learning based framework for the registration of three dimensional multi-modal medical images of the head

Islam

Wijewickrema

O’Leary

2021

Sci Rep

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Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.

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“…It was ranked 6 out of 10 teams who submitted the results. In another study [16], the authors trained a CNN model with good generalisability for predicting the affine transformation in an unsupervised manner. They compared their results with that of SIFT, SURF and Elastix tool.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Feature based Cross-slide Registration

Awan¹,

Raza²,

Lotz³

et al. 2022

Preprint

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Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. Slides stained with different biomarkers are analysed side by side which may reveal unknown relations between the different biomarkers. During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block. The problem is compounded by the fact that tissue contents are likely to change from one section to the next and there may be unique artefacts on some of the slides. This makes registration of each section to a reference section of the same tissue block an important pre-requisite task before any cross-slide analysis. We propose a deep feature based registration (DFBR) method which utilises data-driven features to estimate the rigid transformation. We adopted a multi-stage strategy for improving the quality of registration. We also developed a visualisation tool to view registered pairs of WSIs at different magnifications. With the help of this tool, one can apply a transformation on the fly without the need to generate transformed source WSI in a pyramidal form. We compared the performance of data-driven features with that of hand-crafted features on the COMET dataset. Our approach can align the images with low registration errors. Generally, the success of non-rigid registration is dependent on the quality of rigid registration. To evaluate the efficacy of the DFBR method, the first two steps of the ANHIR winner's framework are replaced with our DFBR to register challenge provided image pairs. The modified framework produce comparable results to that of challenge winning team.

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Learning-Based Affine Registration of Histological Images

Cited by 11 publications

References 19 publications

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

A deep learning based framework for the registration of three dimensional multi-modal medical images of the head

Deep Feature based Cross-slide Registration

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