A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

Peiris, Himashi; Hayat, Munawar; Chen, Zhaolin; Egan, Gary F.; Harandi, Mehrtash

doi:10.1007/978-3-031-16443-9_16

Cited by 102 publications

(29 citation statements)

References 17 publications

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“…Although the development of Swin Transformers [128,134] and windowed attention [153] has moderated computational complexity, the need for more efficient attention mechanisms remains. Fortunately, many mature operators have been developed in CNNs, [220,221] which are beneficial to provide theoretical guidance for more efficient computational mechanisms.…”

Section: Methods Param (M) Flops (G)mentioning

confidence: 99%

“…In addition, they inserted an enhanced Transformer block to supplement the details at the bottleneck. Peiris et al [134] proposed a Volumetric Transformer architecture (VT-UNet) for brain tumor segmentation. The encoder of VT-UNet calculated both local and global attention, while its decoder adopted parallel self-attention and cross-attention to capture and optimize boundary details.…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

“…In addition, they inserted an enhanced Transformer block to supplement the details at the bottleneck. Peiris et al [134] . proposed a Volumetric Transformer architecture (VT‐UNet) for brain tumor segmentation.…”

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

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Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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Section: Methods Param (M) Flops (G)mentioning

confidence: 99%

Section: Brain Tumor Segmentationmentioning

confidence: 99%

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Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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“…• Image-level fusion, such as input data cross talk (Peiris et al, 2021), • Feature-level fusion, such as attention mechanism cross talk (Zhou et al, 2020),…”

Section: Related Workmentioning

confidence: 99%

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

Yang²,

Wang³

et al. 2023

Front. Neurosci.

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Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

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“…Huang et al 10 proposed a new encoder-decoder-based 3D whole volume segmentation framework, this method allows segmentation of colorectal cancerous regions from 3D MR images. Peiris et al 11 proposed a Transformer architecture for volumetric medical image segmentation, it has a U-shaped encoder-decoder design. This method obtained satisfactory results on BraTS 21 and medical segmentation decathlon (pancreas and liver) dataset.…”

Section: Related Workmentioning

confidence: 99%

A semiautomatic segmentation method framework for pelvic bone tumors based on CT‐MR multimodal images

Xia

Yan

et al. 2023

Numer Methods Biomed Eng

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The pelvic structure is complex and the tumor is poorly defined from the surrounding tissues. Finding the exact tumor resection margin based on the surgeon's clinical experience alone is a time‐consuming and difficult task, which is a major factor leading to surgical failure. An accurate method for segmenting pelvic bone tumors is needed. In this paper, a semiautomatic segmentation method for pelvic bone tumors based on CT‐MR multimodal images is presented. The method combines multiple medical prior knowledge and image segmentation algorithms. Finally, the segmentation results are visualized in three dimensions. We tested the proposed method on a collection of 10 cases (97 tumor MR images in total). The segmentation results were compared with the manual annotation of the physicians. On average, our method has an accuracy of 0.9358, a recall of 0.9278, an IOU value of 0.8697, a Dice value of 0.9280, and an AUC value of 0.9632. The average error of the 3D model was within the allowable range of the surgery. The proposed algorithm can accurately segment bone tumors in pelvic MR images regardless of tumor location, size, and other factors. It provides the possibility to assist pelvic bone tumor preservation surgery.

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A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

Cited by 102 publications

References 17 publications

Understanding the brain with attention: A survey of transformers in brain sciences

Understanding the brain with attention: A survey of transformers in brain sciences

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

A semiautomatic segmentation method framework for pelvic bone tumors based on CT‐MR multimodal images

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