Automated multiclass tissue segmentation of clinical brain MRIs with lesions

Weiß, David; Saluja, Rachit; Xie, Lin; Gee, James C.; Sugrue, Leo P.; Pradhan, Abhijeet; Bryan, R. Nick; Rauschecker, Andreas M.; Rudie, Jeffrey D.

doi:10.1016/j.nicl.2021.102769

Cited by 13 publications

(18 citation statements)

References 47 publications

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“…Our results corroborate previous studies that deep learning is effective in medical image segmentation. 21–23,27,28 This study also replicated the results of prior studies showing that U-Nets can segment brain images with high accuracy. 23,27 Our results also corroborate a previous study that showed the effectiveness of 2D CapsNets for segmenting biomedical images, outperforming other deep learning models including U-Nets.…”

Section: Discussionsupporting

confidence: 85%

“…21–23,27,28 This study also replicated the results of prior studies showing that U-Nets can segment brain images with high accuracy. 23,27 Our results also corroborate a previous study that showed the effectiveness of 2D CapsNets for segmenting biomedical images, outperforming other deep learning models including U-Nets. 8 A subsequent study showed that 2D CapsNets were less effective in segmenting heart and brain MRI slices.…”

Section: Discussionsupporting

confidence: 85%

“…[20][21][22][23] The U-Net has shown strong auto-segmentation accuracy across a variety of different imaging modalities and anatomic structures. [21][22][23][24][25][26] Figure 1.B shows the architecture of our 3D U-Net. The input image undergoes 64 convolutions (3×3×3) to generate 64 feature maps.…”

Section: D U-netmentioning

confidence: 99%

“…The U-Net is considered among the highest-performing segmentation algorithms in diagnostic imaging. [20][21][22][23] The U-Net has shown strong auto-segmentation accuracy across a variety of different imaging modalities and anatomic structures. [21][22][23][24][25][26] Figure 1.B shows the architecture of our 3D U-Net.…”

Section: D U-netmentioning

confidence: 99%

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3D Capsule Networks for Brain Image Segmentation

Avesta

Hui

Krumholz

et al. 2022

Preprint

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INTRODUCTION: Segmenting brain structures around a tumor on brain images is important for radiotherapy and surgical planning. Current auto-segmentation methods often fail to segment brain anatomy when it is distorted by tumors. OBJECTIVE: To develop and validate 3D capsule networks (CapsNets) that can segment brain structures with novel spatial features that were not represented in the training data. Methods: We developed, trained, and tested 3D CapsNets using 3430 brain MRIs acquired in a multi-institutional study. We compared our CapsNets with U-Nets using multiple performance measures, including accuracy in segmenting various brain structures, segmenting brain structures with spatial features not represented in the training data, performance when the models are trained using limited data, memory requirements, and computation times. RESULTS: 3D CapsNets can segment third ventricle, thalamus, and hippocampus with Dice scores of 94%, 94%, and 91%, respectively. 3D CapsNets outperform 3D U-Nets in segmenting brain structures that were not represented in the training data, with Dice scores more than 30% higher. 3D CapsNets are also remarkably smaller models compared to 3D U-Nets, with 93% fewer trainable parameters. This led to faster convergence of 3D CapsNets during training, making them faster to train compared to U-Nets. The two models were equally fast during testing. CONCLUSION: 3D CapsNets can segment brain structures with high accuracy, outperform U-Nets in segmenting brain structures with features that were not represented during training, and are remarkably more efficient compared to U-Nets, achieving similar results while their size is one order of magnitude smaller.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionsupporting

confidence: 85%

Section: D U-netmentioning

confidence: 99%

Section: D U-netmentioning

confidence: 99%

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3D Capsule Networks for Brain Image Segmentation

Avesta

Hui

Krumholz

et al. 2022

Preprint

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“…There are different strategies in transfer learning in terms of how the layers are transferred (e.g., directly, fine-tuned, or reinitialized) (Kalmady et al, 2021 ; Prakash et al, 2021 ; Ren et al, 2021 ; Wang et al, 2021 ; Weiss et al, 2021 ). For example, in CNN, there are convolution layers and fully-connected (FC) layers and in transfer learning one can transfer weights associated with convolution layers, FC layers, or both, then decide to freeze or fine-tune them.…”

Section: Resultsmentioning

confidence: 99%

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Ardalan

Subbian

2022

Front. Artif. Intell.

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Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.

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A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images

Kirimtat

Krejcar

2023

Lecture Notes in Computer Science

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Automated multiclass tissue segmentation of clinical brain MRIs with lesions

Cited by 13 publications

References 47 publications

3D Capsule Networks for Brain Image Segmentation

3D Capsule Networks for Brain Image Segmentation

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images

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