A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++

Wang, Jing; Peng, Yanyang; Jing, Shi; Han, Lujun; Li, Tian; Luo, Junpeng

doi:10.1186/s12885-023-11432-x

Cited by 14 publications

(3 citation statements)

References 33 publications

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“…Research findings indicate that UNet++ achieves superior segmentation performance compared to traditional U-Net and other deep learning architectures on public datasets. Wang et al 16 utilized the UNet++ network for deep learning to automatically segment liver tumors in magnetic resonance imaging (MRI). The experimental results demonstrated Dice Similarity Coefficient (DSC) values exceeding .9 for the liver and over .6 for liver tumors, indicating that UNet++ can automatically segment normal liver tissue and liver tumors on MR images.…”

Section: Related Workmentioning

confidence: 99%

Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks

Hou,

Zou,

Wang

2024

Surg Innov

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Objective To propose a transfer learning based method of tumor segmentation in intraoperative fluorescence images, which will assist surgeons to efficiently and accurately identify the boundary of tumors of interest. Methods We employed transfer learning and deep convolutional neural networks (DCNNs) for tumor segmentation. Specifically, we first pre-trained four networks on the ImageNet dataset to extract low-level features. Subsequently, we fine-tuned these networks on two fluorescence image datasets (ABFM and DTHP) separately to enhance the segmentation performance of fluorescence images. Finally, we tested the trained models on the DTHL dataset. The performance of this approach was compared and evaluated against DCNNs trained end-to-end and the traditional level-set method. Results The transfer learning-based UNet++ model achieved high segmentation accuracies of 82.17% on the ABFM dataset, 95.61% on the DTHP dataset, and 85.49% on the DTHL test set. For the DTHP dataset, the pre-trained Deeplab v3 + network performed exceptionally well, with a segmentation accuracy of 96.48%. Furthermore, all models achieved segmentation accuracies of over 90% when dealing with the DTHP dataset. Conclusion To the best of our knowledge, this study explores tumor segmentation on intraoperative fluorescent images for the first time. The results show that compared to traditional methods, deep learning has significant advantages in improving segmentation performance. Transfer learning enables deep learning models to perform better on small-sample fluorescence image data compared to end-to-end training. This discovery provides strong support for surgeons to obtain more reliable and accurate image segmentation results during surgery.

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Section: Related Workmentioning

confidence: 99%

Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks

Hou,

Zou,

Wang

2024

Surg Innov

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“…To comprehensively assess the effectiveness of the proposed method for peach tree disease identification and segmentation, a series of deep learning models known for their broad representativeness and excellent performance in image classification and segmentation tasks were selected as baselines. The baseline models chosen for this study include AlexNet [56], VGGNet [57], ResNet [58], and Effi-cientNet [59] for disease detection, as well as UNet [60], UNet++ [61], and SegNet [62] for lesion segmentation, each demonstrating outstanding performance and wide applicability in their respective application scenarios.…”

Section: Baseline Modelmentioning

confidence: 99%

Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques

Chen,

Lang,

et al. 2024

Agriculture

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An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention mechanism backbone network, an aligned-head module, a Transformer-based semantic segmentation network, and a specially designed alignment loss function. The integration of these technologies not only optimizes the model’s ability to capture subtle disease features but also improves the efficiency of integrating sensor and image data, further enhancing the accuracy of the segmentation tasks. Experimental results demonstrate the superiority of this framework. For disease detection, the proposed method achieved a precision of 94%, a recall of 92%, and an accuracy of 92%, surpassing classical models like AlexNet, GoogLeNet, VGGNet, ResNet, and EfficientNet. In lesion segmentation tasks, the proposed method achieved a precision of 95%, a recall of 90%, and an mIoU of 94%, significantly outperforming models such as SegNet, UNet, and UNet++. The introduction of the aligned-head module and alignment loss function provides an effective solution for processing images lacking sensor data, significantly enhancing the model’s capability to process real agricultural image data. Through detailed ablation experiments, the study further validates the critical role of the aligned-head module and alignment loss function in enhancing model performance, particularly in the attention-head ablation experiment where the aligned-head configuration surpassed other configurations across all metrics, highlighting its key role in the overall framework. These experiments not only showcase the theoretical effectiveness of the proposed method but also confirm its practical value in agricultural disease management practices.

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“…Deep learning (DL) techniques have shown great potential in achieving accurate diagnoses, selecting personalized treatments, and improving prognosis [6,7]. DL models have the ability to capture non-linear relationships in medical data and perform tasks such as classifying images [8], localizing and detecting objects [9][10][11], segmenting data [12][13][14], and generating synthetic data [15,16]. One of the primary objectives is to personalize these tasks for each individual patient, which can lead to better outcomes and an improved quality of life.…”

Section: Introductionmentioning

confidence: 99%

Improving Generation and Evaluation of Long Image Sequences for Embryo Development Prediction

Celard,

Seara Vieira,

Sorribes-Fdez

et al. 2024

Electronics

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Generating synthetic time series data, such as videos, presents a formidable challenge as complexity increases when it is necessary to maintain a specific distribution of shown stages. One such case is embryonic development, where prediction and categorization are crucial for anticipating future outcomes. To address this challenge, we propose a Siamese architecture based on diffusion models to generate predictive long-duration embryonic development videos and an evaluation method to select the most realistic video in a non-supervised manner. We validated this model using standard metrics, such as Fréchet inception distance (FID), Fréchet video distance (FVD), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE). The proposed model generates videos of up to 197 frames with a size of 128×128, considering real input images. Regarding the quality of the videos, all results showed improvements over the default model (FID = 129.18, FVD = 802.46, SSIM = 0.39, PSNR = 28.63, and MSE = 97.46). On the coherence of the stages, a global stage mean squared error of 9.00 was achieved versus the results of 13.31 and 59.3 for the default methods. The proposed technique produces more accurate videos and successfully removes cases that display sudden movements or changes.

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A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++

Cited by 14 publications

References 33 publications

Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks

Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks

Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques

Improving Generation and Evaluation of Long Image Sequences for Embryo Development Prediction

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