Supervoxel Graph Cuts: An Effective Method for GGO Candidate Regions Extraction on CT Images

Lu, Huimin; Kondo, Masashi; Li, Yujie; Tan, Joo Kooi; Kim, Hyoungseop; Murakami, Seiichi; Aoki, Takatoshi; Shoji, K.

doi:10.1109/mce.2019.2941468

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…In addition to FCN-based techniques, several DL-based image segmentation algorithms have been proposed, including polygon-RNN [50], DeepLab V3+ [51], and multi-task network cascades [52]. In recent years, novel approaches for various applications, such as area extraction [53], wound intensity correction [54], and automated lung nodule categorization, have been developed [55]. Although there are positive effects of the therapies discussed above, a few pieces of literature have examined how physicians compute the ROI in skin imaging.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Algorithm for Automated Detection of Skin Lesions

et al. 2021

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Skin cancers are increasing at an alarming rate, and detection in the early stages is essential for advanced treatment. The current segmentation methods have limited labeling ability to the ground truth images due to the numerous noisy expert annotations present in the datasets. The precise boundary segmentation is essential to correctly locate and diagnose the various skin lesions. In this work, the lesion segmentation method is proposed as a Markov decision process. It is solved by training an agent to segment the region using a deep reinforcement-learning algorithm. Our method is similar to the delineation of a region of interest by the physicians. The agent follows a set of serial actions for the region delineation, and the action space is defined as a set of continuous action parameters. The segmentation model learns in continuous action space using the deep deterministic policy gradient algorithm. The proposed method enables continuous improvement in performance as we proceed from coarse segmentation results to finer results. Finally, our proposed model is evaluated on the International Skin Imaging Collaboration (ISIC) 2017 image dataset, Human against Machine (HAM10000), and PH2 dataset. On the ISIC 2017 dataset, the algorithm achieves an accuracy of 96.33% for the naevus cases, 95.39% for the melanoma cases, and 94.27% for the seborrheic keratosis cases. The other metrics are evaluated on these datasets and rank higher when compared with the current state-of-the-art lesion segmentation algorithms.

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