Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

Ghaderi, Sadegh; Ghaderi, Kayvan; Ghaznavi, Hamid

doi:10.21203/rs.3.rs-886978/v1

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Over the past few decades, various medical image segmentation algorithm have been presented, which can be broadly grouped into thresholding (Jain and Singh, 2022;Rawas and El-Zaart, 2022), watershed (Mohanapriya and Kalaavathi, 2019;Sadegh et al, 2022), clustering (Xu et al, 2022;Zhou et al, 2022), conditional random field (Sun et al, 2020;Li et al, 2022), dictionary learning Tang et al, 2021), graph cut (Gamechi et al, 2021;Zhu et al, 2021), region growing (Rundo et al, 2016;Biratu et al, 2021), active contour (Dake et al, 2019;Shahvaran et al, 2021), quantuminspired computing (Sergioli et al, 2021;Amin et al, 2022), computational intelligence (Vijay et al, 2016;. These traditional methods rely on developers to design algorithms for specific applications.…”

Section: Introductionmentioning

confidence: 99%

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Zhang,

Cheng,

Zhou

et al. 2023

Front. Physiol.

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Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People’s Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

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