Semantic Segmentation of Gastrointestinal Tract in MRI Scans Using PSPNet Model With ResNet34 Feature Encoding Network

Sharma, Neha; Gupta, Sheifali; Rajab, Adel; Elmagzoub, Mohamed A.; Rajab, Khairan; Shaikh, Asadullah

doi:10.1109/access.2023.3336862

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Various techniques have been employed, including UNet with an attention mechanism [43], Levit-UNet++ [44], a combination of UNet and Mask RCNN [45], Multiview UNet [46], and an Ensemble Model [47], each achieving different levels of segmentation accuracy with Dice values ranging from 0.36 to 0.88. More recent approaches in 2023 include FPN +Efficient Net B0 [47] with a Dice coefficient of 0.8975, UNet model [48] with a Dice coefficient of 0.8854, and PSPNet+ResNet 34 [49] with a Dice coefficient of 0.8842. The UMobileNet V2 Model, featuring a MobileNetV2 encoder embedded within a UNet architecture, outperforms previous methods with a Dice coefficient of 0.8984, demonstrating promising results in segmenting gastrointestinal structures in the specified dataset.…”

Section: State-of-the-art Comparisonmentioning

confidence: 99%

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

Sharma,

Gupta,

Gupta

et al. 2024

PLoS ONE

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Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients.

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Section: State-of-the-art Comparisonmentioning

confidence: 99%

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

Sharma,

Gupta,

Gupta

et al. 2024

PLoS ONE

Self Cite

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Attentional deep learning based efficient AGSDCLR unet model for gastrointestinal tract segmentation

Devi,

Gunta,

Gutta

et al. 2025

Biomedical Signal Processing and Control

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

Aggarwal,

Gupta,

Kumar

et al. 2024

MBE

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<p>Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew's Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%.</p>

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Semantic Segmentation of Gastrointestinal Tract in MRI Scans Using PSPNet Model With ResNet34 Feature Encoding Network

Cited by 3 publications

References 30 publications

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

Attentional deep learning based efficient AGSDCLR unet model for gastrointestinal tract segmentation

GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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