SIA-Unet: A Unet with Sequence Information for Gastrointestinal Tract Segmentation

Ye, Rongguang; Wang, Ranmin; Guo, Yantong; Chen, Lei

doi:10.1007/978-3-031-20862-1_23

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Table 6 provides a complete overview of recent studies focusing on semantic segmentation of gastrointestinal structures in the UW Madison dataset, highlighting the Dice coefficient as an evaluation metric. 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.…”

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

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“…SIA-Unet additionally contains an attention tool that filters the spatial information of the feature map to extract relevant data. Comprehensive tests on the UW-Madison dataset were carried out to assess the performance of SIA-Unet [20]. In 2022, Nemani P. presented a hybrid CNN-transformer architecture for segmenting distinct organs from images.…”

Section: Related Workmentioning

confidence: 99%

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

Sharma,

Gupta,

Rajab

et al. 2023

IEEE Access

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Gastrointestinal (GI) cancer is the most common cancer in men and women. GI cancers are increasing every year worldwide. In the biomedical industry, Radiation treatment is a frequent choice for treating cancers of the GI tract in which the oncologist focuses the high range of X-ray beams on the tumor while avoiding the healthy organs. Manual segmentation of healthy organs to focus X-ray beams only on the tumor portion is very tedious and time-consuming, which can lead the treatment from a few minutes to hours. Deep learning techniques can concur with this problem by segmentation of healthy organs. This research article proposes a deep learning-based model Pyramid Scene Parsing Network (PSPNet) for segmenting organs such as the stomach, small bowel, and large bowel in the GI tract. The model has been simulated with five feature encoding networks: ResNext 50, Timm_Gernet_S, ResNet 34, EfficientNet B1, and MobileNet V2. These encoders were used for downsampling the feature map in the PSPNet architecture. The implementations have been performed using the UW Madison GI tract dataset, which contains 38,496 MRI scans of cancer patients. The model was evaluated using validation dice, Jaccard, and validation loss. The results reveal that the PSPNet model combined with ResNet 34 as encoder outperforms the other feature encoding networks with validation dice as 0.8842, validation Jaccard as 0.8531, and validation loss as 0.1365. Radiation oncologists can use the proposed model to speed up radiation therapy for cancer treatment.

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“…It additionally contains an attention module that filters the spatial information of the feature map to fetch relevant data. Many trials on the dataset were carried out to assess SIA-Unet's performance [17]. In 2022, Nemani P et al suggested a hybrid CNN-transformer architecture for segmenting distinct organs from images.…”

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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SIA-Unet: A Unet with Sequence Information for Gastrointestinal Tract Segmentation

Cited by 6 publications

References 16 publications

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans

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

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

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