2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

Ottesen, Jon André; Yi, Darvin; Tong, Elizabeth; Iv, Michael; Latysheva, Anna; Saxhaug, Cathrine; Jacobsen, Kari Dolven; Helland, Åslaug; Emblem, Kyrre E.; Rubin, Daniel L.; Bjørnerud, Atle; Zaharchuk, Greg; Grøvik, Endre

doi:10.3389/fninf.2022.1056068

Cited by 11 publications

(6 citation statements)

References 43 publications

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Section: Discussionmentioning

confidence: 95%

“…While earlier studies demonstrated high sensitivity in the detection of BMs, surpassing 80%, they were accompanied by a significant number of FPs and, consequently, exhibited low precision, as indicated in Table 5 ( 7 , 10 , 28 , 29 ). Subsequent research, incorporating multiple modalities, showed improved performance with sensitivities ranging from 82% to 100% and reducing the FP rate to between 0.6 and 1.5 per scan ( 6 , 8 , 30 ). Notably, a recent study, utilizing a single modality, introduced a novel loss function and integrated temporal prior information, achieving exceptional results (sensitivity: 84%; precision: 99%; FP rate: 1) ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, the performance of our algorithm may not be directly compared with previous studies because of a different dataset. However, we ran publicly available algorithms such as 3D U-Net and nnU-Net, which were utilized in prior studies ( 8 , 30 , 32 ), for our dataset, and their performances are inferior to the results of our algorithm ( Supplementary Material S7 ). Consequently, we may conclude that RLK-Unet shows a comparative performance for BM detection and segmentation.…”

Section: Discussionmentioning

confidence: 99%

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Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment

Son,

Joo,

Park

et al. 2024

Front. Oncol.

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Purpose/objective(s)Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment.Methods and materialsA total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed.ResultsRLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84).ConclusionRLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment

Son,

Joo,

Park

et al. 2024

Front. Oncol.

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“…In recent years, deep learning (DL) models have been shown to be a valuable technique in radiology. Besides using DL for detection and segmentation intracranial abnormalities in brain images [17][18], there are also DL models for predictions from input images DL methods have also been used in MRP. There are commercial MRP software that have built-in automated AIF and VOF selection tools that utilize DL [21].…”

Section: Deconvolution Methods Require the Selection Of Two Vascular ...mentioning

confidence: 99%

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics

Talebi

Gai

Sossin

et al. 2023

Preprint

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Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies, including tumor classification, stroke assessment, seizure detection, etc. Currently, most institutions use commercially available software to compute the perfusion parametric maps. Conventionally, the parametric maps are derived by mathematical equations which require the selection of vascular input waveforms. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by an FDA-approved commercial software, we trained a multi-step deep learning (DL) model. The model used each 4D MRP dataset as input, and combined the temporal features extracted from each voxel with spatial information of the brain to predict voxel-wise perfusion parameters. DL-generated rCBV and rCBF maps were evaluated qualitatively and quantitatively. An auxiliary (control) model, with similar architecture, but trained with truncated datasets that had fewer time points, was designed to explore the contribution of temporal features. Our model is based on a multistage encoder-decoder architecture that leverages a 1D convolutional neural network (CNN) as the first encoder to capture temporal information, followed by a 2D U-Net encoder-decoder network to process spatial features. This combination of encoders allows our model to effectively integrate time-varying and spatial data, generating accurate and comprehensive CBV/CBF predictions for the entire brain volume. Our model demonstrates comparable results to that of FDA-approved commercial software.

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“…for the automatic detection and segmentation of BMs from T1 MRI [2,3], T1 and CT [4], multiple T1s and FLAIR [5], using standard 2D, 2.5D and 3D Deep Learning (DL) models.…”

Section: Automated Brain Metastases Segmentation Several Work Have Re...mentioning

confidence: 99%

Automatic Detection and Multi-Component Segmentation of BrainMetastases in Longitudinal MRI

Andrearczyk,

Schiappacasse,

Abler

et al. 2024

Preprint

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Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs. It focuses on several important aspects: identifying and segmenting new lesions for screening and treatment planning, re-segmenting lesions in successive images using prior lesion locations as an additional input channel, and performing multi-component segmentation to distinguish between enhancing tissue, edema, and necrosis. The retrospective data includes 518 metastases in 184 contrast-enhanced T1-weighted MRIs originating from 49 patients (63% male, 37% female). 131 time-points (36 patients, 418 BMs) are used for cross-validation, the remaining 53 time-points (13 patients, 100 BMs) are used for testing. The lesions were manually delineated with label 1: enhancing lesion, label 2: edema, and label 3: necrosis. One-tailed t-tests are used to compare model performance including multiple segmentation and detection metrics. Significance is considered as p$<$0.05. A Dice Similarity Coefficient (DSC) of 0.786 and F1-score of 0.804 are obtained for the segmentation of new lesions. On follow-up scans, the re-segmentation model significantly outperforms the segmentation model (DSC and F1 0.777 and 0.877 vs 0.559 and 0.604). The re-segmentation model also significantly outperforms the simple segmentation model on the enhancing lesion (DSC 0.761 vs 0.525) and edema (0.524 vs 0.465) components, while similar scores are obtained on the necrosis component (0.622 vs 0.627). Additionally, we analyze the correlation between lesion size and segmentation performance, as demonstrated in various studies that highlight the challenges in segmenting small lesions. Our findings indicate that this correlation disappears when utilizing the re-segmentation approach and evaluating with the unbiased normalized DSC. In conclusion, the automated segmentation of new lesions and subsequent re-segmentation in follow-up images was achievable, with high level of performance obtained for single- and multiple-component segmentation tasks.

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2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

Cited by 11 publications

References 43 publications

Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment

Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics

Automatic Detection and Multi-Component Segmentation of BrainMetastases in Longitudinal MRI

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