Vol. 5 No. 3 (2023), Articles
Vol. 5 No. 3 (2023)

Critical Studies on Lesion Segmentation in Medical Images

Articles
Published 22-09-2023
Alok Kumar
Department of Computer Science and Engineering, Government College of Engineering, Anna University, Dharmapuri, India
N. Mahendran
Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Anna University, Karur, India
PDF

Keywords

Lesion segmentation
Medical Images
Survey
CNN

How to Cite

Kumar, Alok, and N. Mahendran. 2023. “Critical Studies on Lesion Segmentation in Medical Images”. Journal of Artificial Intelligence and Capsule Networks 5 (3): 280-97. https://doi.org/10.36548/jaicn.2023.3.005.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Abstract

In medical images, lesion segmentation is used to locate and isolate abnormal structures. It is an essential part of medical image analysis for precise diagnosis and care. However, obstacles exist in medical image lesion segmentation owing to things like image noise, shape and size fluctuation, and poor image quality. Automated lesion segmentation methods include conventional image processing techniques, deep learning (DL) models and machine learning (ML) algorithms to name a few. Thresholding, region growth, and active contour models are examples of conventional methods, while decision trees, random forests, and support vector machines are examples of ML techniques. DL models particularly convolutional neural networks (CNNs), have shown extraordinary performance in lesion segmentation because to their innate potential to autonomously collect high-level characteristics. The objective of the research is to study lesion segmentation in medical images and explore different methods for accurate and precise diagnosis and care.The research focuses on the obstacles faced in lesion segmentation in medical images, such as image noise, shape and size fluctuation, and poor image quality. The research also highlights the need for evaluation metrics, such as sensitivity, specificity, Dice coefficient, and Hausdorff distance, to assess the performance of lesion segmentation algorithms. Additionally, the research emphasizes the importance of annotated datasets for training and evaluating the performance of segmentation algorithms.

PDF

References

K. Yan et al., “Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT,” IEEE Trans. Med. Imaging, vol. 40, no. 10, pp. 2759–2770, 2021, doi: 10.1109/TMI.2020.3047598.

T. Mahmud et al., “CovTANet: A Hybrid Tri-Level Attention-Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans,” IEEE Trans. Ind. Informatics, vol. 17, no. 9, pp. 6489–6498, 2021, doi: 10.1109/TII.2020.3048391.

S. T. Tran, C. H. Cheng, and D. G. Liu, “A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT,” IEEE Access, vol. 9, pp. 3752–3764, 2021, doi: 10.1109/ACCESS.2020.3047861.

T. Mahmud, M. A. Rahman, S. A. Fattah, and S. Y. Kung, “CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans,” IEEE Trans. Artif. Intell., vol. 2, no. 3, pp. 283–297, 2021, doi: 10.1109/TAI.2021.3064913.

Z. Xue et al., “Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images,” IEEE Trans. Med. Imaging, vol. 40, no. 12, pp. 3531–3542, 2021, doi: 10.1109/TMI.2021.3089702.

S. Huang, J. Li, Y. Xiao, N. Shen, and T. Xu, “RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-Lesion Segmentation,” IEEE Trans. Med. Imaging, vol. 41, no. 6, pp. 1596–1607, 2022, doi: 10.1109/TMI.2022.3143833.

Z. Ning, S. Zhong, Q. Feng, W. Chen, and Y. Zhang, “SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image,” IEEE Trans. Med. Imaging, vol. 41, no. 2, pp. 476–490, 2022, doi: 10.1109/TMI.2021.3116087.

X. Zhao et al., “Prior Attention Network for Multi-Lesion Segmentation in Medical Images,” IEEE Trans. Med. Imaging, vol. 41, no. 12, pp. 3812–3823, 2022, doi: 10.1109/TMI.2022.3197180.

X. Wang et al., “Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis,” IEEE Trans. Med. Imaging, vol. 40, no. 9, pp. 2463–2476, Sep. 2021, doi: 10.1109/TMI.2021.3079709.

M. A. Hussain, G. Hamarneh, and R. Garbi, “Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation,” IEEE Trans. Med. Imaging, vol. 40, no. 6, pp. 1555–1567, Jun. 2021, doi: 10.1109/TMI.2021.3060465.

Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection,” IEEE Trans. Biomed. Eng., vol. 64, no. 7, pp. 1558–1567, 2017, doi: 10.1109/TBME.2016.2613502.

G. Santini, N. Moreau, and M. Rubeaux, “Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge,” Sep. 2019,[Online]. Available: http://arxiv.org/abs/1909.00735.

F. Liao, M. Liang, Z. Li, X. Hu, and S. Song, “Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network,” IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 11, pp. 3484–3495, 2019, doi: 10.1109/TNNLS.2019.2892409.

K. Mahajan, M. Sharma, and L. Vig, “Meta-DermDiagnosis: Few-shot skin disease identification using meta-learning,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2020-June, pp. 3142–3151, 2020, doi: 10.1109/CVPRW50498.2020.00373.

M. Zlocha, Q. Dou, and B. Glocker, “Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11769 LNCS, pp. 402–410, 2019, doi: 10.1007/978-3-030-32226-7_45.