UNet-VGG16 with transfer learning  for MRI-based brain tumor segmentation

Pravitasari, Anindya Apriliyanti; Iriawan, Nur; Almuhayar, Mawanda; Azmi, Tasya Auliya Ulul; Irhamah, Irhamah; Fithriasari, Kartika; Purnami, Santi Wulan; Ferriastuti, Widiana

doi:10.12928/telkomnika.v18i3.14753

Cited by 105 publications

(56 citation statements)

References 16 publications

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“…The most common approach to apply a prior-sharing strategy—and, in general, transfer learning—was fine-tuning all the parameters of a pretrained CNN [ 29 , 31 , 32 , 33 , 35 , 39 , 71 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 ] (80% of all prior-sharing methods). Other approaches utilized Bayesian graphical models [ 37 , 38 , 120 , 121 ], graph neural networks [ 122 ], kernel methods [ 64 , 123 ], multilayer perceptrons [ 124 ], and Pearson-correlation methods [ 125 ].…”

Section: Resultsmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer’s diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

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Section: Resultsmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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“…U-Net was proposed in 2015 as a sliding window convolutional network, dedicatedly developed to examine test images of the ISIC challenge database [12]. In recent years, due to its performance and significance, a considerable number of modified versions of U-Net schemes are available for other image databases [13][14][15][16].…”

Section: U-netmentioning

confidence: 99%

“…In the literature, a number of pre-trained and customary CNN segmentation schemes are available, which examine biomedical images recorded using varied imaging modalities [8][9][10][11][12]. The development of a customary CNN scheme for a chosen image is computationally complex, and hence, pre-trained CNN designs are extensively adopted by most researchers due to its availability, performance, and adaptability toward varied imaging modalities [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…In this research, we adopted well-known CNN schemes, such as SegNet [9,10], U-Net [11][12][13], and VGG-UNet [14][15][16][17], to extract leukocyte fragments from images with enhanced accuracy. After extracting the leukocyte fragment, a relative assessment between the extracted section and the available Ground-Truth (GT) is done to confirm the performance of the CNN scheme.…”

Section: Introductionmentioning

confidence: 99%

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Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

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Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

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“…For brain tumor segmentation in MRI images, the researchers in Dong et al (2017) employed U-Net. In contrast, in Pravitasari et al (2020) they employed U-Net and VGG16 network in the encoder, and in Aboelenein et al (2020) the researchers built a Hybrid Two-Track U-Net (HTTU-Net) by using Leaky Relu activation and batch normalization. In Li et al (2018) they proposed a hybrid densely connected U-Net (H-DenseU-Net) that consisted of a densely connected network (DenseNet) and U-Net for automatic liver lesion segmentation from CT scans by replacing the convolutional layer with dense blocks in the encoder.…”

Section: Related Workmentioning

confidence: 99%

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Abedalla

Abdullah

Al‐Ayyoub

et al. 2021

PeerJ Computer Science

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Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image segmentation system. Deep learning algorithms have recently shown outstanding performance for segmentation tasks, especially semantic segmentation networks that provide pixel-level image understanding. By introducing the first fully convolutional network (FCN) for semantic image segmentation, several segmentation networks have been proposed on its basis. One of the state-of-the-art convolutional networks in the medical image field is U-Net. This paper presents a novel end-to-end semantic segmentation model, named Ens4B-UNet, for medical images that ensembles four U-Net architectures with pre-trained backbone networks. Ens4B-UNet utilizes U-Net’s success with several significant improvements by adapting powerful and robust convolutional neural networks (CNNs) as backbones for U-Nets encoders and using the nearest-neighbor up-sampling in the decoders. Ens4B-UNet is designed based on the weighted average ensemble of four encoder-decoder segmentation models. The backbone networks of all ensembled models are pre-trained on the ImageNet dataset to exploit the benefit of transfer learning. For improving our models, we apply several techniques for training and predicting, including stochastic weight averaging (SWA), data augmentation, test-time augmentation (TTA), and different types of optimal thresholds. We evaluate and test our models on the 2019 Pneumothorax Challenge dataset, which contains 12,047 training images with 12,954 masks and 3,205 test images. Our proposed segmentation network achieves a 0.8608 mean Dice similarity coefficient (DSC) on the test set, which is among the top one-percent systems in the Kaggle competition.

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UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

Cited by 105 publications

References 16 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

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