DCNet: Densely Connected Deep Convolutional Encoder–Decoder Network for Nasopharyngeal Carcinoma Segmentation

Yang, Li; Han, Guanghui; Liu, Xiujian

doi:10.3390/s21237877

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…An alternative metric for training loss is focal Dice loss, which could alleviate the imbalance between empirically defined subtypes [52]. Models that extract semantic features could integrate spatial information to improve sensitivity to tumors at smaller sizes and tissue boundaries, making it worthwhile to validate their efficacy in the TNBC population [53,54]. Finally, a systematic comparison of our model to the conventional models using the same datasets would better evaluate our model.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Xu,

Rauch,

Mohamed

et al. 2023

Cancers

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Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients’ treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Xu,

Rauch,

Mohamed

et al. 2023

Cancers

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show abstract

“…Feature fusion and skip connections are used to enhance the performance of (convolutional) neural network models by mitigating the vanishing gradient problem in deep networks [13], [14], [15], [37], [38], [39]. In this concept, features from previous layer(s) are fused in the next layer(s) either by performing summation or concatenation.…”

Section: Feature Fusion Network Architecturesmentioning

confidence: 99%

DC-DFFN: Densely Connected Deep Feature Fusion Network With Sign Agnostic Learning for Implicit Shape Representation

Basher

Boutellier

2023

IEEE Access

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Reconstructing 3D surfaces from raw point cloud data is still a challenging and complex problem in computer vision and graphics. Recently emerged neural implicit representations model 3D surfaces implicitly in arbitrary resolution and diverse topologies. In this domain, most of the studies have so far used a single latent code-based variational auto-encoder (VAE) or auto-decoder (AD) architectures, or architectures similar to UNets. Due to the deep architectures of the existing approaches, gradients and/or input information can vanish while passing through the layers, which can cause suboptimal learning at training time and consequently low performance at test time. As a countermeasure, skip connections and feature fusion have been used in related application fields of convolutional neural networks. In this study, we embrace this idea and propose a novel densely connected deep feature fusion network, DC-DFFN, architecture for implicit shape representation. In the experimental results we show that DC-DFFN outperforms baseline approaches in terms visual reconstruction quality and quantitatively based on several measures. In addition, the proposed approach provides faster convergence during training compared to the baseline approaches. The DC-DFFN architecture has been implemented in PyTorch and is available as open source.

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“…Among them, many fully supervised algorithms have been proposed for nasopharyngeal carcinoma segmentation. Convolutional neural networks (CNN) [32] are an effective image segmentation method that captures contextual semantics by computing high-level feature maps [33,34]. Since the pioneering CNN algorithm by Lecun et al, in 1990, more and more improved CNN algorithms for image segmentation have been proposed.…”

Section: Fully-supervisedmentioning

confidence: 99%

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

Chen

Han

Lin

et al. 2022

Sensors

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Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

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DCNet: Densely Connected Deep Convolutional Encoder–Decoder Network for Nasopharyngeal Carcinoma Segmentation

Cited by 10 publications

References 39 publications

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

DC-DFFN: Densely Connected Deep Feature Fusion Network With Sign Agnostic Learning for Implicit Shape Representation

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

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