Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts

Yang, Zhen; Zhao, Yuqian; Liao, Miao; Di, Shuanhu; Zeng, Ye-zhan

doi:10.1016/j.bspc.2021.102670

Cited by 25 publications

(7 citation statements)

References 26 publications

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“…Data augmentation did not help much in the performance of this work but with the help of data augmentation methods, maybe the results of transformers can be increased. Using semi‐automatic [50] or interactive methods including snake [51, 52] can help to achieve better results than fully automatic methods.…”

Section: Discussionmentioning

confidence: 99%

Brain tumour segmentation of MR images based on custom attention mechanism with transfer‐learning

Vatanpour,

Haddadnia

2023

IET Image Processing

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The automatic segmentation of brain tumours is a critical task in patient disease management. It can help specialists easily identify the location, size, and type of tumour to make the best decisions regarding the patients' treatment process. Recently, deep learning methods with attention mechanism helped increase the performance of segmentation models. The proposed method consists of two main parts: the first part leverages a deep neural network architecture for biggest tumour detection (BTD) and in the second part, ResNet152V2 makes it possible to segment the image with the attention block and the extraction of local and global features. The custom attention block is used to consider the most important parts in the slices, emphasizing on related information for segmentation. The results show that the proposed method achieves average Dice scores of 0.81, 0.87 and 0.91 for enhancing core, tumour core and whole tumour on BraTS2020 dataset, respectively. Compared with other segmentation approaches, this method achieves better performance on tumour core and whole tumour. Further comparisons on BraTS2018 and BraTS2017 validation datasets show that this method outperforms other models based on Dice score and Hausdorff criterion.

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Section: Discussionmentioning

confidence: 99%

Brain tumour segmentation of MR images based on custom attention mechanism with transfer‐learning

Vatanpour,

Haddadnia

2023

IET Image Processing

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“…Once blood smear images are preprocessed, the next stage is to perform GC segmentation to determine the affected regions in the blood smear images. The GC-based segmentation model lies in the extraction of diseased regions from the target image with detailed information [ 21 ]. It is widely used to segment medical images due to its benefit of attaining global optimum solutions.…”

Section: Methodsmentioning

confidence: 99%

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

Dutta

Mageswari²,

Gayathri

et al. 2022

Computational Intelligence and Neuroscience

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Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.

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“…It can objectify pixel clusters referring to 12 pairs of ribs [35,36]. In several recent studies have been published using 3D region growing method to segment tumors and nodules in limited medical images according to the 3D imaging system in the medical field without extra training, which achieved significant performance in clinical decision [37,38]. In this study, it was designed that if the examined pixel value was 255 and it was adjacent to the seed pixel indicating the ribs, the pixel was included in the same index of the label.…”

Section: D-region Growing For Sequence Labelingmentioning

confidence: 99%

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

Seol

Kim

Park

et al. 2022

Sensors

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This paper proposes a development of automatic rib sequence labeling systems on chest computed tomography (CT) images with two suggested methods and three-dimensional (3D) region growing. In clinical practice, radiologists usually define anatomical terms of location depending on the rib’s number. Thus, with the manual process of labeling 12 pairs of ribs and counting their sequence, it is necessary to refer to the annotations every time the radiologists read chest CT. However, the process is tedious, repetitive, and time-consuming as the demand for chest CT-based medical readings has increased. To handle the task efficiently, we proposed an automatic rib sequence labeling system and implemented comparison analysis on two methods. With 50 collected chest CT images, we implemented intensity-based image processing (IIP) and a convolutional neural network (CNN) for rib segmentation on this system. Additionally, three-dimensional (3D) region growing was used to classify each rib’s label and put in a sequence label. The IIP-based method reported a 92.0% and the CNN-based method reported a 98.0% success rate, which is the rate of labeling appropriate rib sequences over whole pairs (1st to 12th) for all slices. We hope for the applicability thereof in clinical diagnostic environments by this method-efficient automatic rib sequence labeling system.

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Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts

Cited by 25 publications

References 26 publications

Brain tumour segmentation of MR images based on custom attention mechanism with transfer‐learning

Brain tumour segmentation of MR images based on custom attention mechanism with transfer‐learning

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

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