Multi-view Unet for Automated GI Tract Segmentation

Li, Huifu; Liu, Jiyao

doi:10.1109/prai55851.2022.9904201

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…RLE-encoding provides compact representations of pixel-wise masks, and a function has been created to convert them into masked images with unique labels for each class, enhancing visual distinction between segments. [4,20] The Cropping function is designed to remove black borders from images, using three input parameters: unmasked image, masked image, and pixel tolerance. It identifies the border's boundaries and crops the images accordingly.…”

Section: Datasetmentioning

confidence: 99%

Gastro-Intestine Tract Image Segmentation using Semantic Segmentation and U-Net: Enhancing Cancer Diagnosis and Treatment

2024

IRJMETS

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In this project, a deep learning model based on the U-Net architecture was created for image segmentation of MRI scans. The dataset comprised MRI scans collected over six months from patients undergoing MRI-guided radiotherapy. A segmentation model was developed and implemented to accurately differentiate various structures in the gastrointestinal (GI) tract. In other word, to develop a model that can automatically identify and differentiate the stomach, large intestines, and small intestines on MRI scans for cancer patients. The model's performance was evaluated using appropriate metrics and compared with existing methods. The potential of the model in enhancing cancer diagnosis and treatment through integration into medical imaging systems was explored. The project successfully implemented the UNet model that achieved satisfactory results using the Dice score and IOU score to evaluated the performance of the model, the Dice score reached to 0.8833 on the training and 0.8533 validation sets, respectively, and an IOU score of 0.8233on the training and 0.7933 on validation sets,.

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Section: Datasetmentioning

confidence: 99%

Gastro-Intestine Tract Image Segmentation using Semantic Segmentation and U-Net: Enhancing Cancer Diagnosis and Treatment

2024

IRJMETS

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“…For instance, Wang et al (2020) employed a multi scale context guided deep network, Li and Liu (2022) employed a multi-view U-Net for GI tract segmentation, Guggari et al (2022) proposed RU-Net for GI tract image segmentation,Zhang et al (2017) proposed the Gastric precancerous diseases classification using CNN, Asperti and Mastronardo (2017) shows the effectiveness of data augmentation for detection of gastrointestinal diseases, Ali et al (2021) shows detection and segmentation of artefact and disease instances in gastrointestinal endoscopy, Nemani and Vollala (2022) employed LeViT U-Net++ for GI tract and Jha et al (2019) showed the usage of ResUnet++ architecture for polyp detection and segmentation.…”

Section: Related Workmentioning

confidence: 99%

LWU-Net approach for Efficient Gastro-Intestinal Tract Image Segmentation in Resource-Constrained Environments

Sai,

Punn

2023

Preprint

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This paper introduces a Lightweight U-Net (LWU-Net) method for efficient gastro-intestinal tract segmentation in resource-constrained environments. The proposed model seeks to strike a balance between computational efficiency, memory efficiency, and segmentation accuracy. The model achieves competitive performance while reducing computational power needed with improvements including depth-wise separable convolutions and optimised network depth. The evaluation is conducted using data from a Kaggle competition-UW Madison gastrointestinal tract image segmentation, demonstrating the model’s effectiveness and generalizability. The findings demonstrate that the LWU-Net model has encouraging promise for precise medical diagnoses in resource-constrained settings, enabling effective image segmentation with slightly less than a fifth of as many trainable parameters as the U-Net model.

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“…They investigated and fused multiple 2.5D data production methodologies to efficiently utilize the association of nearby pictures. They suggested a technique for combining 2.5D and 3D findings [21]. In 2022, Chia B et al introduced two baseline methods: a UNet trained on a ResNet50 backbone and a more economical and streamlined UNet.…”

Section: Literature Reviewmentioning

confidence: 99%

EfficientNetB0 cum FPN Based Semantic Segmentation of Gastrointestinal Tract Organs in MRI Scans

et al. 2023

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The segmentation of gastrointestinal (GI) organs is crucial in radiation therapy for treating GI cancer. It allows for developing a targeted radiation therapy plan while minimizing radiation exposure to healthy tissue, improving treatment success, and decreasing side effects. Medical diagnostics in GI tract organ segmentation is essential for accurate disease detection, precise differential diagnosis, optimal treatment planning, and efficient disease monitoring. This research presents a hybrid encoder–decoder-based model for segmenting healthy organs in the GI tract in biomedical images of cancer patients, which might help radiation oncologists treat cancer more quickly. Here, EfficientNet B0 is used as a bottom-up encoder architecture for downsampling to capture contextual information by extracting meaningful and discriminative features from input images. The performance of the EfficientNet B0 encoder is compared with that of three encoders: ResNet 50, MobileNet V2, and Timm Gernet. The Feature Pyramid Network (FPN) is a top-down decoder architecture used for upsampling to recover spatial information. The performance of the FPN decoder was compared with that of three decoders: PAN, Linknet, and MAnet. This paper proposes a segmentation model named as the Feature Pyramid Network (FPN), with EfficientNet B0 as the encoder. Furthermore, the proposed hybrid model is analyzed using Adam, Adadelta, SGD, and RMSprop optimizers. Four performance criteria are used to assess the models: the Jaccard and Dice coefficients, model loss, and processing time. The proposed model can achieve Dice coefficient and Jaccard index values of 0.8975 and 0.8832, respectively. The proposed method can assist radiation oncologists in precisely targeting areas hosting cancer cells in the gastrointestinal tract, allowing for more efficient and timely cancer treatment.

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Multi-view Unet for Automated GI Tract Segmentation

Cited by 8 publications

References 17 publications

Gastro-Intestine Tract Image Segmentation using Semantic Segmentation and U-Net: Enhancing Cancer Diagnosis and Treatment

Gastro-Intestine Tract Image Segmentation using Semantic Segmentation and U-Net: Enhancing Cancer Diagnosis and Treatment

LWU-Net approach for Efficient Gastro-Intestinal Tract Image Segmentation in Resource-Constrained Environments

EfficientNetB0 cum FPN Based Semantic Segmentation of Gastrointestinal Tract Organs in MRI Scans

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