Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging

Park, Yae Won; Jun, Yohan; Lee, Yangho; Han, Kyunghwa; An, Chansik; Ahn, Sung Soo; Hwang, Dosik; Lee, Seung Koo

doi:10.1007/s00330-021-07783-3

Cited by 34 publications

(35 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The L-DICE segmentation performance of our HD-BM algorithm (0.78 or 0.79 in both test sets) was in line with previous studies (0.6–0.82). 17 , 19–23 , 26 As expected, on a case-by-case basis our approach showed a better result with a median C-DICE-score of 0.9 in the institutional test set, which is comparable to the results in larger primary brain tumors. 8 We observed comparatively lower L-DICE as compared to C-DICE values, which can be expected because many patients have multiple lesions of different volumes: When calculating the C-DICE, the L-DICE of the bigger lesions influenced the metric more than smaller lesions, due to the greater number of true positive/false negative/FP voxels of the large lesions.…”

Section: Discussionsupporting

confidence: 80%

“…This also applied to other works with higher sensitivity, which however also featured about eight times (7.8 FPs/scan) 20 and two times (1.5 FPs/scan) 25 more FPs/scan than HD-BM. A recent study by Park et al 26 reported a high sensitivity of 0.931 and also low FP/scan with 0.59. They developed multiple methods, the best using a combination of 3D black blood and 3D gradient echo (GRE) imaging techniques, while their model based only the 3D GRE sequences (like ours), reached a sensitivity of 0.768, which is slightly lower than the L-Sensitivity in our test sets.…”

Section: Discussionmentioning

confidence: 94%

See 1 more Smart Citation

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Pflüger

Wald

Isensee

et al. 2022

Neuro-Oncology Advances

View full text Add to dashboard Cite

Background Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM. Methods A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR-signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity). Results The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2cm³;p=0.2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR=0.6–0.91) and 0.90 (IQR=0.85-0.94) in the institutional as well as 0.79 (IQR=0.67–0.82) and 0.84 (IQR=0.76-0.89) in the external test-dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR=0.63–0.92) and 0.79 (IQR=0.63–0.93) in the institutional test-dataset, as compared to 0.85 (IQR=0.76–0.94) and 0.76 (IQR=0.68–0.88) in the external test-dataset. The median C-DICE for NEE was 0.96 (IQR=0.92–0.97) in the institutional test-dataset as compared to 0.85 (IQR=0.72–0.91) in the external test-dataset. Conclusion The developed ANN based algorithm (publicly available at www .github.com/NeuroAI-HD/HD-BM/link_to_repository_provided_after_acceptance) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.

show abstract

Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 94%

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Pflüger

Wald

Isensee

et al. 2022

Neuro-Oncology Advances

View full text Add to dashboard Cite

show abstract

“…Driven by the rapid growth in computer science, the performance of deep learning is on par with or even outperforms radiologists in visual identification, which can perform automated data-oriented feature extraction and thus learning directly the most relevant feature representation from the input images ( 11 , 12 ). The U-Net algorithm is one of the most commonly used deep learning-based convolutional neural networks (CNNs) ( 13 ), which shows potential in detection, segmentation, and classification of metastatic lesions on MRI images such as brain metastases ( 14 , 15 ) and liver metastases ( 16 ). Concerning the automated bone metastasis analysis using the deep learning technique, the research trend is mainly on BS ( 17 , 18 ) and single-photon emission computerized tomography (SPECT) images ( 19 , 20 ); less attention has been paid to the diagnosis of mpMRI ( 21 , 22 ).…”

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning

et al. 2021

View full text Add to dashboard Cite

ObjectiveTo establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using diffusion-weighted imaging (DWI) and T1 weighted imaging (T1WI) images.MethodsThe model consisted of two 3D U-Net algorithms. A total of 859 patients with clinically suspected or confirmed PCa between January 2017 and December 2020 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. Then, 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from January to May 2021 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision, and F1-score were assessed at the lesion level.ResultsThe pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85, and the HD values were <15 mm. In the testing set, the AUC of the metastases detection at the patient level were 0.85 and 0.80 on DWI and T1WI images. At the lesion level, the F1-score achieved 87.6% and 87.8% concerning metastases detection on DWI and T1WI images, respectively. In the external dataset, the AUC of the model for M-staging was 0.94 and 0.89 on DWI and T1WI images.ConclusionThe deep learning-based 3D U-Net network yields accurate detection and segmentation of pelvic bone metastases for PCa patients on DWI and T1WI images, which lays a foundation for the whole-body skeletal metastases assessment.

show abstract

“…Simultaneous use of multiple imaging modalities could be the solution to this. Reviewing previous works, the sensitivity for detection of smaller brain lesions (<3 mm) with 3D U-Net, whether trained with black-blood or gradient echo modalities, decreased significantly compared to larger brain lesions (≥10 mm, 0.981, 3-10 mm 0.829, <3 mm 0.235) [59]. The same trend could be observed in studies performed with 2-stage MetNet (≥6 mm 0.99, 3-6 mm 0.87, ≤3 mm 0.25) [60] or GoogLeNet [61].…”

Section: Limitation Of This Studymentioning

confidence: 87%

Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

Chang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.

show abstract

Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging

Cited by 34 publications

References 35 publications

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning

Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

Contact Info

Product

Resources

About