A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images

Kar, Mithun Kumar; Nath, Malaya Kumar; Neog, Debanga Raj

doi:10.1007/s42979-021-00784-5

Cited by 30 publications

(14 citation statements)

References 139 publications

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“…Research shows that the U-Net architecture has high accuracy and robustness in retinal blood vessel image segmentation tasks. 16 Using a multiscale approach enables the model to predict more blood vessels than a single-scale model, improving the level of detail obtained. In the segmentation of retinal vessels, various U-shaped networks with multi-scale models have produced promising results.…”

Section: U-shaped Networkmentioning

confidence: 99%

A combination of multi‐scale and attention based on the U‐shaped network for retinal vessel segmentation

Zhang,

Lan,

Sun

et al. 2024

Int J Imaging Syst Tech

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Automated partitioning of retinal vessels depicted in fundus images is beneficial in the detection of specific ailments like hypertension and diabetes. However, retinal vessel images have the problems of a large semantic range, more spatial detail, and limited differentiation among the blood vessels and surroundings, which make vessel segmentation challenging. To overcome these obstacles, we designed a new U‐shaped network named SMP‐Net. First, we propose the sequencer‐convolution (SC) module to obtain the ability to extract both local and global features, thereby improving segmentation accuracy. The SC module is used to filter out shallow noise and enable the fusion of deep and shallow features in the maximum skip connection of the U‐shaped network. Then, the residual multi‐kernel pooling (MP) module is designed to obtain additional contextual information while also mitigating the loss of spatial information caused by constant pooling and convolution to improve vessel coherence. Finally, the pixel attention (PA) module redistributes the weight of each pixel using an element‐wise product multiplication operation. This increases the weight of the vascular feature pixels and improves the ability to identify blood vessels in blurred backgrounds. The proposed method has been demonstrated to be effective through sufficient experimentation on publicly available retinal datasets such as DRIVE, STARE, and CHASE_DB1.

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Section: U-shaped Networkmentioning

confidence: 99%

A combination of multi‐scale and attention based on the U‐shaped network for retinal vessel segmentation

Zhang,

Lan,

Sun

et al. 2024

Int J Imaging Syst Tech

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“…On this basis, Kar et al discussed deep learning-based semantic segmentation techniques. This technique uses the deep neural network structure to segment images and discusses the basic theory related to deep learning methods used in semantic segmentation [ 8 ]. Chen et al proposed the DeepLap model, which is applied to the cavity convolution in diffusion-convolutional neural networks (DCNNs), and combined with the conditional random field (CRF) to overcome the shortcomings of DCNNs.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

Geng¹,

Yan

2022

Computational Intelligence and Neuroscience

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This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.

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“…Here, data augmentation evolved as a simple, yet powerful technique [1,16]. In deep learning-based semantic image segmentation, geometric transformations are most common [8]. This holds particularly true for surgical applications.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of surgical hyperspectral images under geometric domain shifts

Sellner¹,

Seidlitz²,

Studier-Fischer³

et al. 2023

Preprint

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Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, however -although common in real-world open surgeries due to variations in surgical procedures or situs occlusions -remain a topic largely unaddressed in the field. To address this gap in the literature, we (1) present the first analysis of state-of-the-art (SOA) semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data, and (2) address generalizability with a dedicated augmentation technique termed 'Organ Transplantation' that we adapted from the general computer vision community. According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data. Surprisingly, this holds true not only for conventional RGB data (drop of Dice similarity coefficient (DSC) by 46 %) but also for HSI data (drop by 45 %), despite the latter's rich information content per pixel. Using our augmentation scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data. The simplicity and effectiveness of our augmentation scheme makes it a valuable network-independent tool for addressing geometric domain shifts in semantic scene segmentation of intraoperative data. Our code and pretrained models are available at https://github.com/IMSY-DKFZ/htc. Equal contribution.

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A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images

Cited by 30 publications

References 139 publications

A combination of multi‐scale and attention based on the U‐shaped network for retinal vessel segmentation

A combination of multi‐scale and attention based on the U‐shaped network for retinal vessel segmentation

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

Semantic segmentation of surgical hyperspectral images under geometric domain shifts

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