TransFusionNet: Semantic and Spatial Features Fusion Framework for Liver Tumor and Vessel Segmentation Under JetsonTX2

Wang, Xun; Zhang, Xudong; Gan, Wang; Zhang, Ying; Shi, Xin; Dai, Huanhuan; Liu, Min; Wang, Zixuan; Meng, Xiangyu

doi:10.1109/jbhi.2022.3207233

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…Medical image learning ( Zhou et al, 2021 ; Qiao et al, 2022 ) is developing rapidly based on the emergence of machine learning ( Song et al, 2021a ; Song et al, 2021b ; Xie et al, 2021 ; Song et al, 2022a ; Song et al, 2022b ; Li et al, 2022 ; Wang et al, 2022 ) and neural network ( Meng et al, 2021a ; Meng et al, 2021b ; Wang et al, 2021 ; Qiao et al, 2022 ), thereby dramatically assists radiologist alleviating workload during reading computed tomography (CT) ( Meng et al, 2022 ) images in computer-aided detection/diagnosis (CADe/CADx) ( Wang et al, 2022 ). Meanwhile, universal lesion detection (ULD) ( Li et al, 2022 ) is an important topic to develop a universal or multicategory CADe/CADx 3D framework, which needs to feed an annotated dataset on computed tomography (CT) ( Yan et al, 2019 ; Li et al, 2020 ; Li et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A universal lesion detection method based on partially supervised learning

Wang,

Shi,

Meng

et al. 2023

Front. Pharmacol.

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Partially supervised learning (PSL) is urgently necessary to explore to construct an efficient universal lesion detection (ULD) segmentation model. An annotated dataset is crucial but hard to acquire because of too many Computed tomography (CT) images and the lack of professionals in computer-aided detection/diagnosis (CADe/CADx). To address this problem, we propose a novel loss function to reduce the proportion of negative anchors which is extremely likely to classify the lesion area (positive samples) as a negative bounding box, further leading to an unexpected performance. Before calculating loss, we generate a mask to intentionally choose fewer negative anchors which will backward wrongful loss to the network. During the process of loss calculation, we set a parameter to reduce the proportion of negative samples, and it significantly reduces the adverse effect of misclassification on the model. Our experiments are implemented in a 3D framework by feeding a partially annotated dataset named DeepLesion, a large-scale public dataset for universal lesion detection from CT. We implement a lot of experiments to choose the most suitable parameter, and the result shows that the proposed method has greatly improved the performance of a ULD detector. Our code can be obtained at https://github.com/PLuld0/PLuldl.

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Section: Introductionmentioning

confidence: 99%

A universal lesion detection method based on partially supervised learning

Wang,

Shi,

Meng

et al. 2023

Front. Pharmacol.

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“…These models require training a network with billions of parameters to learn common features across various data samples and transferring weights to a specific task to capture the unique properties. Some notable models are transformer-based models [23], [24], which were first proposed in the natural language processing field. They include dense models such as GPT-3 [25], Gopher [26], and the sparse model based on the mixture of expert (MOE) model [27], [28].…”

Section: Introductionmentioning

confidence: 99%

“…With the maturity of deep learning methods in different fields [11,12], the Cox model, based on an artificial neural network, has received extensive attention from researchers. To the best of our knowledge, the earliest application of artificial neural networks for survival analysis is Faraggi et al [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Attention-Mechanism Based Cox Survival Model by Exploiting Pan-Cancer Empirical Genomic Information

Meng

Wang

Zhang

et al. 2022

Cells

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Cancer prognosis is an essential goal for early diagnosis, biomarker selection, and medical therapy. In the past decade, deep learning has successfully solved a variety of biomedical problems. However, due to the high dimensional limitation of human cancer transcriptome data and the small number of training samples, there is still no mature deep learning-based survival analysis model that can completely solve problems in the training process like overfitting and accurate prognosis. Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data. This model adopts a novel attention mechanism and takes full advantage of the adversarial transfer learning strategy. We trained the model on 16 types of TCGA cancer RNA-seq data sets. Experiments show that our module outperformed state-of-the-art survival analysis models such as the Cox proportional hazard model (Cox-ph), Cox-lasso, Cox-ridge, Cox-nnet, and VAECox on the concordance index. In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions.

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Hepatic and portal vein segmentation with dual‐stream deep neural network

Xu,

Jiang,

et al. 2024

Medical Physics

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BackgroundLiver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time‐consuming, and the labeling results of different clinicians are prone to inconsistencies. Hence, developing an automatic segmentation algorithm for hepatic and PVs on CT images has attracted the attention of researchers. Unfortunately, existing deep learning based automatic segmentation methods are prone to misclassifying peripheral vessels into wrong categories.PurposeThis study aims to provide a fully automatic and robust semantic segmentation algorithm for hepatic and PVs, guiding subsequent preoperative planning. In addition, to address the deficiency of the public dataset for hepatic and PV segmentation, we revise the annotations of the Medical Segmentation Decathlon (MSD) hepatic vessel segmentation dataset and add the masks of the hepatic veins (HVs) and PVs.MethodsWe proposed a structure with a dual‐stream encoder combining convolution and Transformer block, named Dual‐stream Hepatic Portal Vein segmentation Network, to extract local features and long‐distance spatial information, thereby extracting anatomical information of hepatic and portal vein, avoiding misdivisions of adjacent peripheral vessels. Besides, a multi‐scale feature fusion block based on dilated convolution is proposed to extract multi‐scale features on expanded perception fields for local features, and a multi‐level fusing attention module is introduced for efficient context information extraction. Paired t‐test is conducted to evaluate the significant difference in dice between the proposed methods and the comparing methods.ResultsTwo datasets are constructed from the original MSD dataset. For each dataset, 50 cases are randomly selected for model evaluation in the scheme of 5‐fold cross‐validation. The results show that our method outperforms the state‐of‐the‐art Convolutional Neural Network‐based and transformer‐based methods. Specifically, for the first dataset, our model reaches 0.815, 0.830, and 0.807 at overall dice, precision, and sensitivity. The dice of the hepatic and PVs are 0.835 and 0.796, which also exceed the numeric result of the comparing methods. Almost all the p‐values of paired t‐tests on the proposed approach and comparing approaches are smaller than 0.05. On the second dataset, the proposed algorithm achieves 0.749, 0.762, 0.726, 0.835, and 0.796 for overall dice, precision, sensitivity, dice for HV, and dice for PV, among which the first four numeric results exceed comparing methods.ConclusionsThe proposed method is effective in solving the problem of misclassifying interlaced peripheral veins for the HV and PV segmentation task and outperforming the comparing methods on the relabeled dataset.

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TransFusionNet: Semantic and Spatial Features Fusion Framework for Liver Tumor and Vessel Segmentation Under JetsonTX2

Cited by 10 publications

References 45 publications

A universal lesion detection method based on partially supervised learning

A universal lesion detection method based on partially supervised learning

A Novel Attention-Mechanism Based Cox Survival Model by Exploiting Pan-Cancer Empirical Genomic Information

Hepatic and portal vein segmentation with dual‐stream deep neural network

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