Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance for Image Alignment

Waldmannstetter, Diana; Navarro, Fernando A.; Wiestler, Benedikt; Kirschke, Jan S.; Sekuboyina, Anjany; Molero, Ester; Menze, Bjoern

doi:10.1007/978-3-030-50120-4_8

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…In recent years, deep convolutional neural networks have been widely adopted in medical imaging applications for both localization [4], [5], [6], [7], [8] and segmentation [9], [10], [11], [12]. These methods focus either on localization or segmentation.…”

Section: Introductionmentioning

confidence: 99%

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Navarro¹,

Sasahara²,

Shit³

et al. 2022

Preprint

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Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of 0.9260 ± 0.18% on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.

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Section: Introductionmentioning

confidence: 99%

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Navarro¹,

Sasahara²,

Shit³

et al. 2022

Preprint

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“…The identification of pathological tissues in post-operative acquisitions can be improved by comparing MRI images obtained at subsequent stages of neurosurgical treatments (Verma et al, 2008 ). Registration algorithms are used to establish correspondences for a precise visual inspection between the subsequent MRI scans (Waldmannstetter et al, 2020 ). Mass effects, pathology resection, and tumor regrowth produce large deformations in the close-to-tumor regions (van der Hoorn et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

Canalini

Klein

Waldmannstetter

et al. 2022

Front. Neuroimaging

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Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.

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“…Although this is a very appealing problem to solve and an accurate solution could contribute on improving computational studies focusing on brain tumor analyses [4], [8], [9], there is only some limited literature on this problem [3], [5], [6], [10], [11] with concerns about their reproducibility on multiinstitutional data. Towards this end, this paper describes the design of the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as a computational competition that utilizes ample retrospective multi-institutional mpMRI data from patient populations of distinct demographics, to establish a public benchmark environment for deformable registration algorithms.…”

Section: Introductionmentioning

confidence: 99%

The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients

Baheti¹,

Waldmannstetter²,

Chakrabarty³

et al. 2021

Preprint

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Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. This paper describes the first Brain Tumor Sequence Registration (BraTS-Reg) challenge, focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a brain diffuse glioma. The BraTS-Reg challenge intends to establish a public benchmark environment for deformable registration algorithms. The associated dataset comprises de-identified multi-institutional multi-parametric MRI (mpMRI) data, curated for each scan's size and resolution, according to a common anatomical template. Clinical experts have generated extensive annotations of landmarks points within the scans, descriptive of distinct anatomical locations across the temporal domain. The training data along with these ground truth annotations will be released to participants to design and develop their registration algorithms, whereas the annotations for the validation and the testing data will be withheld by the organizers and used to evaluate the containerized algorithms of the participants. Each submitted algorithm will be quantitatively evaluated using several metrics, such as the Median Absolute Error (MAE), Robustness, and the Jacobian determinant.

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Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance for Image Alignment

Cited by 8 publications

References 16 publications

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients

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