Deep learning with multiresolution handcrafted features for brain MRI segmentation

Mecheter, Imene; Abbod, Maysam F.; Amira, Abbes; Zaidi, Habib

doi:10.1016/j.artmed.2022.102365

Cited by 13 publications

(2 citation statements)

References 60 publications

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“…Magnetic resonance images are segmented to either extract the required information from the MR modality itself or to perform mapping from another modality to MR modality such as pseudo-CT generation [1] and MR-based attenuation correction for positron emission tomography [2]. With this increasing interest in MR modality in the clinical domain, multiple deep convolutional neural networks (CNN) have been successfully applied to perform MR images segmentation [3][4][5][6]. However, CNNs have the limitations of heavy computations and the need of powerful computing resources with large memory allocation.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

Mecheter,

Abbod,

Zaidi

et al. 2023

CAAI Trans on Intel Tech

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Magnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1‐weighted (T1‐w) to T2‐weighted (T2‐w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation‐based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi‐parametric segmentation model is proposed using T2‐w MR as an intensity‐based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2‐w‐based augmentation has no significant impact on the performance of T1‐w MR segmentation, it helps in improving T2‐w MR segmentation and developing a multi‐sequences segmentation model.

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Section: Introductionmentioning

confidence: 99%

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

Mecheter,

Abbod,

Zaidi

et al. 2023

CAAI Trans on Intel Tech

Self Cite

View full text Add to dashboard Cite

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“…Furthermore, computer-aided diagnosis can play an important auxiliary role in medical teaching, surgical planning, surgical simulation and various medical research (Menze et al, 2015 ; Havaei et al, 2016 ). Accurate and automatic segmentation of brain tumors in different modalities of brain MRI is a challenging task in medical image analysis (Mecheter et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

MMGan: a multimodal MR brain tumor image segmentation method

Gao,

Li,

Zhang

et al. 2023

Front. Hum. Neurosci.

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Computer-aided diagnosis has emerged as a rapidly evolving field, garnering increased attention in recent years. At the forefront of this field is the segmentation of lesions in medical images, which is a critical preliminary stage in subsequent treatment procedures. Among the most challenging tasks in medical image analysis is the accurate and automated segmentation of brain tumors in various modalities of brain tumor MRI. In this article, we present a novel end-to-end network architecture called MMGan, which combines the advantages of residual learning and generative adversarial neural networks inspired by classical generative adversarial networks. The segmenter in the MMGan network, which has a U-Net architecture, is constructed using a deep residual network instead of the conventional convolutional neural network. The dataset used for this study is the BRATS dataset from the Brain Tumor Segmentation Challenge at the Medical Image Computing and Computer Assisted Intervention Society. Our proposed method has been extensively tested, and the results indicate that this MMGan framework is more efficient and stable for segmentation tasks. On BRATS 2019, the segmentation algorithm improved accuracy and sensitivity in whole tumor, tumor core, and enhanced tumor segmentation. Particularly noteworthy is the higher dice score of 0.86 achieved by our proposed method in tumor core segmentation, surpassing those of stateof-the-art models. This study improves the accuracy and sensitivity of the tumor segmentation task, which we believe is significant for medical image analysis. And it should be further improved by replacing different loss functions such as cross-entropy loss function and other methods.

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FM-LiteLearn: A Lightweight Brain Tumor Classification Framework Integrating Image Fusion and Multi-teacher Distillation Strategies

Tan,

Cai,

Zhao

et al. 2024

Lecture Notes in Computer Science

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Deep learning with multiresolution handcrafted features for brain MRI segmentation

Cited by 13 publications

References 60 publications

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

MMGan: a multimodal MR brain tumor image segmentation method

FM-LiteLearn: A Lightweight Brain Tumor Classification Framework Integrating Image Fusion and Multi-teacher Distillation Strategies

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