DeSeg: auto detector-based segmentation for brain metastases

Yu, Hui; Zhang, Zhongzhou; Xia, Wenjun; Liu, Yan; Liu, Lun-Xin; Luo, Wuman; Zhou, Jiliu; Zhang, Yi

doi:10.1088/1361-6560/acace7

Cited by 10 publications

(7 citation statements)

References 37 publications

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“…In recent years, medical image analysis algorithms based on deep learning have reached optimal results. Deep learning has shown powerful capabilities in medical image segmentation (Clark et al 2012, Zhang et al 2022, Yu et al 2023 and classification (Gao et al 2023. Deep learning-based registration algorithms have surpassed traditional methods in registration accuracy and far exceeded traditional registration algorithms in terms of inference time.…”

Section: Related Work 21 Optimization-based Affine Registration Methodsmentioning

confidence: 99%

Affine medical image registration with fusion feature mapping in local and global

Ji,

Yang

2024

Phys. Med. Biol.

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Objective. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships of the features due to inductive biases, such as weight sharing and locality. This is not conducive to affine registration tasks. Therefore, the evolution of real-time and high-accuracy affine medical image registration algorithms is necessary for registration applications. Approach. In this paper, we propose a deep learning-based coarse-to-fine global and local feature fusion architecture for fast affine registration, and we use an unsupervised approach for end-to-end training. We use multiscale convolutional kernels as our elemental convolutional blocks to enhance feature extraction. Then, to learn the long-range spatial relationships of the features, we propose a new affine registration framework with weighted global positional attention that fuses global feature mapping and local feature mapping. Moreover, the fusion regressor is designed to generate the affine parameters. Main results. The additive fusion method can be adaptive to global mapping and local mapping, which improves affine registration accuracy without the center of mass initialization. In addition, the max pooling layer and the multiscale convolutional kernel coding module increase the ability of the model in affine registration. Significance. We validate the effectiveness of our method on the OASIS dataset with 414 3D MRI brain maps. Comprehensive results demonstrate that our method achieves state-of-the-art affine registration accuracy and very efficient runtimes.

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Section: Related Work 21 Optimization-based Affine Registration Methodsmentioning

confidence: 99%

Affine medical image registration with fusion feature mapping in local and global

Ji,

Yang

2024

Phys. Med. Biol.

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“…Since BM usually has a solid sphere-like shape, the lesion can be identified via its geometric center, called center point. Following our center point detection strategy [15], we present a center saliency map generation (CSMG) algorithm to detect the center point. The center saliency map indicates the probability of each pixel being the center point, which can be calculated from a feature map by any feature encoder.…”

Section: Center-point-based Lesion Identificationmentioning

confidence: 99%

“…where L cls is the cross-entropy loss [15]. C was the class number; p i was the predicted probability for the i − th class; y i was the i − th class label.…”

Section: Loss Functionmentioning

confidence: 99%

A Pilot Study: Deep Multi-Instance Learning for Origin Tracing of Brain Metastases

Yu,

Zhang,

Yang

et al. 2024

Preprint

Self Cite

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Treatment decisions for brain metastasis heavily rely on identifying the primary site, which is typically accomplished through biomarker-based techniques such as genomics and histopathology. However, limited healthcare resources sometimes can hinder their availability. Therefore, we innovatively transform origin tracing into an image classification task. Based on T1ce-MRI, we develop a non-invasive and cost-effective pipeline, called deep multi-instance learning (DMIL). The DMIL-based pipeline includes three steps: pre-processing, training and testing. Particularly, in pre-processing, mix-modal data decoration is proposed to learn multiple modal knowledge. For DMIL training, center-point-based lesion identification is employed to automatically crop ROIs, eliminating the need for manual intervention. Additionally, self-adaptive lesion classification aims to achieve slice-wise origin tracing. During the inference stage, to address the uncertainty stemming from heterogeneity within a patient's volume, we design a voting majority mechanism to make final patient-wise predictions. Evaluated on the clinical dataset, our DMIL-based pipeline demonstrated promising results. The best patient-wise results achieved at 87.27% (accuracy), 85.00% (PPV) and 83.33% (sensitivity).

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“…Experiments are designed to demonstrate the effectiveness of the multi-task deep learning model proposed in this study. The ResNet [ 33 ], DeSeg [ 23 ], UNet3+ [ 34 ], radiomics model [ 14 ], RN-GAP [ 35 ], DenseNet [ 36 ], SE-Net [ 37 ], UNet [ 38 ], RA-UNet [ 39 ], Swin-UNet [ 40 ], TransUNet [ 41 ] deep learning models are set to implement separate single-task training for the classification and segmentation tasks.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Li et al [ 22 ] developed a two-stage deep learning model for the automatic detection and segmentation of BMs in MR images, yielding a segmentation Dice score of 0.81 and a detection precision of 0.56. Yu et al [ 23 ] proposed a novel deep learning model to incorporate object-level detection into pixel-wise segmentation to simultaneously localize BMs and delineate contours, achieving a detection sensitivity of 0.91 and a detection precision of 0.77 on a small BM group and a segmentation Dice score of 0.86 on a large BM group. Hsu et al [ 24 ] used 3D V-Net to segment BMs on MR and CT images.…”

Section: Introductionmentioning

confidence: 99%

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis

Zhou,

Wang,

Zhao

et al. 2023

J Transl Med

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Background The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks. Methods To comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights. Results The experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization. Conclusion With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.

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DeSeg: auto detector-based segmentation for brain metastases

Cited by 10 publications

References 37 publications

Affine medical image registration with fusion feature mapping in local and global

Affine medical image registration with fusion feature mapping in local and global

A Pilot Study: Deep Multi-Instance Learning for Origin Tracing of Brain Metastases

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis

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