Recognition of necrotic regions in MRI images of chronic spinal cord injury based on superpixel

Bao, Xing-Xing; Zhao, Chen; Bao, Shu-Sheng; Rao, Jia‐Sheng; Yang, Zhaoyang; Li, Xiaoguang

doi:10.1016/j.cmpb.2022.107252

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Over-segment: the input breast image will be over-segmented into the corresponding superpixel mask which includes prior shape information of tumors. In this study, simple linear iterative clustering (SLIC) algorithm (Achanta et al 2012), a widely used technique in the field of medical image processing (Gao et al 2017, Chandra et al 2022, Di et al 2022, Bao et al 2023 due to its low computational complexity and promising performance, is employed to produce superpixel masks for breast images. The SLIC algorithm first transforms the color space of an image from RGB to CIELAB and then generates homogeneous regions with different sizes by clustering similar pixels.…”

Section: Overviewmentioning

confidence: 99%

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

Lin

Huang

et al. 2023

Phys. Med. Biol.

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Due to the blurry edges and uneven shape of breast tumors, breasttumor segmentation can be a challenging task. Recently, deep convolution networks(DCNs) based approaches achieve satisfying segmentation results. However, thelearned shape information of breast tumors might be lost owing to the successiveconvolution and down-sampling operations, resulting in limited performance. To thisend, we propose a novel Shape-Guided Segmentation (SGS) framework that guidesthe segmentation networks to be shape-sensitive to breast tumors by prior shapeinformation. Different from usual segmentation networks, we guide the networks tomodel shape-shared representation with the assumption that shape information ofbreast tumors can be shared among samples. Specifically, on the one hand, we proposea Shape Guiding Block (SGB) to provide shape guidance through a superpixel poolingunpooling operation and attention mechanism. On the other hand, we introducethe Shared Classification Layer (SCL) to address the problems brought by the SGB,including feature inconsistency and additional computational cost. Additionally, theproposed SGB and SCL can be effortlessly incorporated into mainstream segmentationnetworks (e.g., UNet) to compose the SGS, facilitating compact shape-friendlyrepresentation learning. Experiments conducted on a private dataset and a publicdataset demonstrate the effectiveness of the SGS compared to other advanced methods.The source code is made available at https://github.com/TxLin7/Shape-Seg.

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Section: Overviewmentioning

confidence: 99%

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

Lin

Huang

et al. 2023

Phys. Med. Biol.

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“…In research conducted by Bao et al [24] to identify necrotic areas in magnetic resonance imaging (MRI) images of chronic spinal injuries. This method focuses on the accurate and automatic location of necrotic areas on MRI images.…”

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confidence: 99%

The object detection model uses combined extraction with KNN and RF classification

Kurniati,

Manongga,

Sembiring

et al. 2024

IJEECS

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Object detection plays an important role in various fields. Developing detection models for 2D objects that experience rotation and texture variations is a challenge. In this research, the initial stage of the proposed model integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) texture feature extraction to obtain feature vectors. The next stage is classifying features using k-nearest neighbors (KNN) and random forest (RF), as well as voting ensemble (VE). System testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower. Although GLCM features improve performance on both algorithms, KNN is more consistent. The VE approach provides the best performance with an accuracy of 93.9% and an F1-score of 93.8%, this shows the effectiveness of the ensemble technique in increasing object detection accuracy. This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification.

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A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN

Li,

Zhou,

Tong

2023

Communications in Computer and Information Science

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Recognition of necrotic regions in MRI images of chronic spinal cord injury based on superpixel

Cited by 5 publications

References 38 publications

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

The object detection model uses combined extraction with KNN and RF classification

A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN

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