Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.

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M2UNet++

10.4018/978-1-7998-7709-7.ch015 ◽

2022 ◽

pp. 256-273

Author(s):

Devidas Tulshiram Kushnure ◽

Sanjay Nilkanth Talbar

Keyword(s):

Decision Making ◽

Deep Learning ◽

Quantitative Result ◽

Manual Segmentation ◽

Liver Segmentation ◽

Multi Scale ◽

Hepatic Diseases ◽

Segmentation Accuracy ◽

High Level ◽

Result Analysis

Liver segmentation is instrumental for decision making in the medical realm for the diagnosis and treatment planning of hepatic diseases. However, the manual segmentation of the hundreds of CT images is tedious for medical experts. Thus, it hampers the segmentation accuracy and is reliant on opinion of the operator. This chapter presents the deep learning-based modified multi-scale UNet++ (M2UNet++) approach for automatic liver segmentation. The multi-scale features were modified channel-wise using adaptive feature recalibration to improve the representation of the high-level semantic information of the skip pathways and improved the segmentation performance with fewer computational overheads. The experimental results proved the model's efficacy on the publicly available 3DIRCADb dataset, which offers significant complexity and variations. The model's dice coefficient value is 97.28% that is 7.64%, and 2.24% improved from the UNet and UNet++ model. The quantitative result analysis shows that the M2UNet++ model outperforms the state-of-the-art methods proposed for liver segmentation.

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Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

Frontiers in Neuroscience ◽

10.3389/fnins.2021.758887 ◽

2021 ◽

Vol 15 ◽

Author(s):

Cheng Wan ◽

Jiasheng Wu ◽

Han Li ◽

Zhipeng Yan ◽

Chenghu Wang ◽

...

Keyword(s):

Loss Function ◽

High Myopia ◽

Area Under Curve ◽

Multi Scale ◽

Retinal Pathology ◽

Segmentation Accuracy ◽

Parapapillary Atrophy ◽

Contour Information ◽

Novel Algorithm ◽

Visual Geometry

In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).

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Medical Image Segmentation Algorithm Based on Multi-scale Color Wavelet Texture

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2021.15.99 ◽

2021 ◽

Vol 15 ◽

pp. 928-935

Author(s):

Qi Zhang ◽

Yan Li

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Medical Image ◽

Feature Detection ◽

Medical Image Segmentation ◽

Block Matching ◽

Bright Spot ◽

Multi Scale ◽

Segmentation Accuracy ◽

Tumor Mri

In order to improve the segmentation accuracy of brain tumor magnetic resonance medical image, a segmentation method of brain tumor magnetic resonance medical image based on multi-scale color wavelet texture features is proposed. The segmentation model of brain tumor magnetic resonance medical image is established, and the motion damage information of brain tumor magnetic resonance medical image is adaptively fused in the ultrasound imaging environment. The medical image information is enhanced by using the motion skeletal muscle block matching technology. According to the suspicious point feature matching method of brain tumor, the fusion detection and processing of brain tumor magnetic resonance medical image are carried out. The multi-scale color wavelet texture feature detection method is used to extract the image features of brain tumor MRI points, and the CT bright spot features are used to analyze the features of brain tumor MRI medical images. Combined with the adaptive neural network training method, the automatic detection of brain tumor magnetic resonance medical image is completed, and the suspected brain tumor points are extracted, so as to realize the segmentation of brain tumor magnetic resonance medical image. Simulation results show that the proposed method can effectively improve the segmentation accuracy of brain tumor MRI medical image, and has high resolution and accuracy for suspicious brain tumor detection.

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Automatic segmentation algorithm for breast cell image based on multi-scale CNN and CSS corner detection

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200041 ◽

2020 ◽

Vol 24 (3) ◽

pp. 195-203

Author(s):

Haoyang Tang ◽

Cong Song ◽

Meng Qian

Keyword(s):

Automatic Segmentation ◽

Scale Space ◽

Segmentation Algorithm ◽

Corner Detection ◽

Adherent Cells ◽

Multi Scale ◽

Cell Image ◽

Curvature Scale Space ◽

Segmentation Accuracy ◽

Initial Segmentation

As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.

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Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network

Micromachines ◽

10.3390/mi12121478 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1478

Author(s):

Zhenwei Li ◽

Mengli Jia ◽

Xiaoli Yang ◽

Mengying Xu

Keyword(s):

Histogram Equalization ◽

Training Data ◽

Stochastic Gradient Descent ◽

Practical Significance ◽

Morphological Transformation ◽

Small Vessels ◽

Multi Scale ◽

Retinal Blood Vessels ◽

Segmentation Accuracy ◽

Adaptive Gamma Correction

The accurate segmentation of retinal blood vessels in fundus is of great practical significance to help doctors diagnose fundus diseases. Aiming to solve the problems of serious segmentation errors and low accuracy in traditional retinal segmentation, a scheme based on the combination of U-Net and Dense-Net was proposed. Firstly, the vascular feature information was enhanced by fusion limited contrast histogram equalization, median filtering, data normalization and multi-scale morphological transformation, and the artifact was corrected by adaptive gamma correction. Secondly, the randomly extracted image blocks are used as training data to increase the data and improve the generalization ability. Thirdly, stochastic gradient descent was used to optimize the Dice loss function to improve the segmentation accuracy. Finally, the Dense-U-net model was used for segmentation. The specificity, accuracy, sensitivity and AUC of this algorithm are 0.9896, 0.9698, 0.7931, 0.8946 and 0.9738, respectively. The proposed method improves the segmentation accuracy of vessels and the segmentation of small vessels.

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A review on segmentation of lung parenchyma based on deep learning methods

Journal of X-Ray Science and Technology ◽

10.3233/xst-210956 ◽

2021 ◽

pp. 1-15

Author(s):

Wenjun Tan ◽

Peifang Huang ◽

Xiaoshuo Li ◽

Genqiang Ren ◽

Yufei Chen ◽

...

Keyword(s):

Computed Tomography ◽

Lung Parenchyma ◽

Dice Coefficient ◽

Multi Scale ◽

Regional Area ◽

Digital Medicine ◽

Segmentation Accuracy ◽

Feature Information ◽

Ct Segmentation ◽

Modified Forms

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improve the quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this review paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.

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Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

Frontiers in Physics ◽

10.3389/fphy.2021.742296 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhentao Pang ◽

Hang Zhang ◽

Yu Wang ◽

Letian Zhang ◽

Yingchun Wu ◽

...

Keyword(s):

Digital Holography ◽

Statistical Data ◽

Threshold Method ◽

Particle Detection ◽

Training Time ◽

Multi Scale ◽

Segmentation Accuracy ◽

Deep Layers ◽

Secondary Breakup ◽

Boundary Distance

Accurate particle detection is a common challenge in particle field characterization with digital holography, especially for gel secondary breakup with dense complex particles and filaments of multi-scale and strong background noises. This study proposes a deep learning method called Mo-U-net which is adapted from the combination of U-net and Mobilenetv2, and demostrates its application to segment the dense filament-droplet field of gel drop. Specially, a pruning method is applied on the Mo-U-net, which cuts off about two-thirds of its deep layers to save its training time while remaining a high segmentation accuracy. The performances of the segmentation are quantitatively evaluated by three indices, the positive intersection over union (PIOU), the average square symmetric boundary distance (ASBD) and the diameter-based prediction statistics (DBPS). The experimental results show that the area prediction accuracy (PIOU) of Mo-U-net reaches 83.3%, which is about 5% higher than that of adaptive-threshold method (ATM). The boundary prediction error (ASBD) of Mo-U-net is only about one pixel-wise length, which is one third of that of ATM. And Mo-U-net also shares a coherent size distribution (DBPS) prediction of droplet diameters with the reality. These results demonstrate the high accuracy of Mo-U-net in dense filament-droplet field recognition and its capability of providing accurate statistical data in a variety of holographic particle diagnostics. Public model address: https://github.com/Wu-Tong-Hearted/Recognition-of-multiscale-dense-gel-filament-droplet-field-in-digital-holography-with-Mo-U-net.

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