LMNS-Net: Lightweight Multiscale Novel Semantic-Net deep learning approach used for automatic pancreas image segmentation in CT scan images

Paithane, Pradip; Kakarwal, S. N.

doi:10.1016/j.eswa.2023.121064

Cited by 10 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to improve uniformity and generalizability, future research must incorporate the following elements: our work's evaluation studies only used one kind of modality, so it would be interesting to include a multi-modality imaging of dental radiography while assessing the models. Furthermore, additional recent CNN models, such as Ref-UNet 3+ [46], CMFCUNet [47] and LMNS-Net [48], could be used to improve performance. Hypermodels (two or more models combined in a single detection) can be used to improve the accuracy of cavity segmentation.…”

Section: Future Workmentioning

confidence: 99%

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Alharbi,

AlRugaibah,

Alhasson

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisionlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images’ binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.

show abstract

Section: Future Workmentioning

confidence: 99%

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Alharbi,

AlRugaibah,

Alhasson

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…They reported that this method is more performant than previous methods [4]. Paithane and Kakarwal used 12-layer deep learning networks with four convolution layers in the LMNS-Net model for pancreas segmentation and obtained a membrane similarity index score of 88.68 ± 57.49% [13]. In a new deep learning method proposed for gross tumor volume segmentation from MRI images (GTV) of pancreatic cancer patients, 126 image sets of 21 patients were used as the data set.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation

Uslucuk,

Öcal

2023

OPRD

View full text Add to dashboard Cite

The pancreas is one of the vital organs in the human body. It has an essential role in the digestive system and endocrine system. Diseases such as cancer, diabetes, hormonal problems, pancreatitis, and digestive problems occur in pancreatic disorders. In detecting pancreatic disorders, first blood and urine tests are requested. If further examination is needed, CT (Computed Tomography), MR (Magnetic Resonance), and EUS (Endoscopic Ultrasonography) imaging methods are used. Pancreas segmentation is generally the process of defining and drawing the lines of the pancreas from medical images such as CT and MRI. The size and shape of the pancreas varies from person to person. Manual segmentation of the pancreas is time-consuming and varies between physicians. Recently, deep learning-based segmentation methods that achieve high-performance results in organ segmentation have become trendy. In this study, Unet and Vnet architectures were comparatively analyzed on the NIH-CT-82 dataset. As a result of the ablation studies, a validation sensitivity of 0.9978 and a validation loss of 0.041 were obtained in the Unet architecture. In the training with the Vnet architecture, 0.9975 validation sensitivity and 0.046 validation loss values were obtained, respectively.

show abstract

Thresholding-accelerated convolutional neural network for aeroengine turbine blade segmentation

Zheng,

Tang,

Sun

2024

Expert Systems with Applications

View full text Add to dashboard Cite

LMNS-Net: Lightweight Multiscale Novel Semantic-Net deep learning approach used for automatic pancreas image segmentation in CT scan images

Cited by 10 publications

References 40 publications

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation

Thresholding-accelerated convolutional neural network for aeroengine turbine blade segmentation

Contact Info

Product

Resources

About