Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Hu, Szu-Yeu; Weng, Wei-Hung; Lu, Shyue-Kung; Cheng, Yueh-Hung; Xiao, Furen; Hsu, Feng‐Ming; Lu, Jen-Tang

doi:10.1007/978-3-030-32486-5_8

Cited by 16 publications

(39 citation statements)

References 11 publications

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“…The ABS system, extended from our previous collaboration work, 26 in this study is a deep learning-based segmentor using multimodal imaging from MR and CT, and ensemble neural networks as illustrated in Figure 1 . Specifically, we combined DeepMedic 34 and three-dimensional (3D) U-Net 35 architecture.…”

Section: Methodsmentioning

confidence: 99%

“…By adopting the ensemble technique, the overall performance in both lesion segmentation and detection can be further improved and reach a balance between high sensitivity and high specificity. 26 , 36 We preprocessed the images for training and validation and trained the neural networks in accordance with a previous work, 26 but in a multitasking fashion where brain metastases, meningiomas, and acoustic neuromas were handled simultaneously. For each case, after performing rigid image registration between CT and MR images using mutual information optimization (mean registration error is on the order of submillimeter), 37 , 38 we resampled the images to an isotropic resolution of 1 × 1 × 1 mm 3 .…”

Section: Methodsmentioning

confidence: 99%

“… 22–25 In particular, convolutional U-Net and DeepMedic architecture have been utilized for brain metastasis segmentation. 26–32 …”

mentioning

confidence: 99%

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Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Xiao

Cheng

et al. 2021

Neuro-Oncology

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Background Stereotactic radiosurgery (SRS), a validated treatment for brain tumors, requires accurate tumor contouring. This manual segmentation process is time-consuming and prone to substantial inter-practitioner variability. Artificial intelligence (AI) with deep neural networks have increasingly been proposed for use in lesion detection and segmentation but have seldom been validated in a clinical setting. Methods We conducted a randomized, cross-modal, multi-reader, multi-specialty, multi-case study to evaluate the impact of AI assistance on brain tumor SRS. A state-of-the-art auto-contouring algorithm built on multi-modality imaging and ensemble neural networks was integrated into the clinical workflow. Nine medical professionals contoured the same case series in two reader modes (assisted or un-assisted) with a memory washout period of 6 weeks between each section. The case series consisted of ten algorithm-unseen cases, including five cases of brain metastases, three of meningiomas and two of acoustic neuromas. Among the nine readers, three experienced experts determined the ground truths of tumor contours. Results With the AI assistance, the inter-reader agreement significantly increased (Dice similarity coefficient [DSC] from 0.86 to 0.90, P<0.001). Algorithm-assisted physicians demonstrated a higher sensitivity for lesion detection than un-assisted physicians (91.3% versus 82.6%, P=0.030). AI assistance improved contouring accuracy, with an average increase in DSC of 0.028, especially for physicians with less SRS experience (average DSC from 0.847 to 0.865, P=0.002). In addition, AI assistance improved efficiency with a median of 30.8%-time savings. Less-experienced clinicians gained prominent improvement on contouring accuracy but less benefit in reduction of working hours. By contrast, SRS specialists had a relatively minor advantage in DSC, but greater timesaving with the aid of AI. Conclusions Deep learning neural networks can be optimally utilized to improve accuracy and efficiency for the clinical workflow in brain tumor SRS.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Xiao

Cheng

et al. 2021

Neuro-Oncology

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“…In [15], the improved patch sampling technique was proposed. Several works [16], [17], [18] proposed to reweight a loss function to achieve higher segmentation scores.…”

Section: A Related Workmentioning

confidence: 99%

Systematic Clinical Evaluation of a Deep Learning Method for Medical Image Segmentation: Radiosurgery Application

Shirokikh

Dalechina

Shevtsov

et al. 2022

IEEE J. Biomed. Health Inform.

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We systematically evaluate a Deep Learning model in a 3D medical image segmentation task. With our model, we address the flaws of manual segmentation: high inter-rater contouring variability and time consumption of the contouring process. The main extension over the existing evaluations is the careful and detailed analysis that could be further generalized on other medical image segmentation tasks. Firstly, we analyze the changes in the inter-rater detection agreement. We show that the model reduces the number of detection disagreements by 48% (p < 0.05). Secondly, we show that the model improves the interrater contouring agreement from 0.845 to 0.871 surface Dice Score (p < 0.05). Thirdly, we show that the model accelerates the delineation process between 1.6 and 2.0 times (p < 0.05). Finally, we design the setup of the clinical experiment to either exclude or estimate the evaluation biases; thus, preserving the significance of the results. Besides the clinical evaluation, we also share intuitions and practical ideas for building an efficient DLbased model for 3D medical image segmentation.

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“…In [13], the improved patch sampling technique was proposed. Finally, several works [14], [15], [16] proposed to reweight a loss function to achieve higher segmentation scores.…”

Section: A Related Workmentioning

confidence: 99%

Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application

Shirokikh¹,

Dalechina²,

Shevtsov³

et al. 2021

Preprint

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We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our segmentation method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow. With our method, we address the relative drawbacks of manual segmentation: high inter-rater contouring variability and high time consumption of the contouring process. The main extension over the existing evaluations is the careful and detailed analysis that could be further generalized on other medical image segmentation tasks. Firstly, we analyze the changes in the inter-rater detection agreement. We show that the segmentation model reduces the ratio of detection disagreements from 0.162 to 0.085 (p < 0.05). Secondly, we show that the model improves the inter-rater contouring agreement from 0.845 to 0.871 surface Dice Score (p < 0.05). Thirdly, we show that the model accelerates the delineation process in between 1.6 and 2.0 times (p < 0.05). Finally, we design the setup of the clinical experiment to either exclude or estimate the evaluation biases, thus preserve the significance of the results. Besides the clinical evaluation, we also summarize the intuitions and practical ideas for building an efficient DL-based model for 3D medical image segmentation.

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Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Cited by 16 publications

References 11 publications

Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Systematic Clinical Evaluation of a Deep Learning Method for Medical Image Segmentation: Radiosurgery Application

Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application

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