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DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans
Ye Zhang, Yulu Gong, Dongji Cui, Xinrui Li, Xinyu Shen
TL;DR
Manual GI-tract delineation for radiotherapy planning is time-consuming and subject to inter-observer variability. The paper proposes automated MRI segmentation using Inception-V4, UNet++ with a VGG19 encoder, and Edge UNet, with separate 2.5D and grayscale processing. The reported results describe Edge UNet as performing exceptionally well on grayscale images and UNet++ with VGG19 as excelling on 2.5D images.
Problem
Radiotherapy planning relies on manual GI-tract delineation, a time-consuming process susceptible to inter-observer variability.
Source: §1 · p. 1Method
The paper proposes an automated MRI-segmentation model combining Inception-V4 classification with UNet++ and VGG19 for 2.5D data and Edge UNet for grayscale data.
Source: §1 · p. 1Results
Edge UNet performed exceptionally well in grayscale segmentation, while UNet++ with VGG19 excelled on 2.5D images.
Source: §5 · p. 7Takeaways & Limitations
The authors present the model as an efficient and accurate tool for clinicians to streamline radiotherapy planning.
Source: §5 · p. 7
Abstract
from arXiv · showhide
Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise radiotherapy planning for optimal treatment outcomes. This paper introduces a cutting-edge approach to automate the segmentation of GI tract regions in magnetic resonance imaging (MRI) scans. Leveraging advanced deep learning architectures, the proposed model integrates Inception-V4 for initial classification, UNet++ with a VGG19 encoder for 2.5D data, and Edge UNet for grayscale data segmentation. Meticulous data preprocessing, including innovative 2.5D processing, is employed to enhance adaptability, robustness, and accuracy. This work addresses the manual and time-consuming segmentation process in current radiotherapy planning, presenting a unified model that captures intricate anatomical details. The integration of diverse architectures, each specializing in unique aspects of the segmentation task, signifies a novel and comprehensive solution. This model emerges as an efficient and accurate tool for clinicians, marking a significant advancement in the field of GI tract image segmentation for radiotherapy planning.
1. INTRODUCTION
Manual GI-tract delineation for radiotherapy planning is time-consuming and susceptible to inter-observer variability. The paper proposes automated MRI segmentation using complementary deep-learning architectures and preprocessing approaches.
1. INTRODUCTION
- The architecture combines Inception-V4, UNet++ with VGG19, and Edge UNet for classification and segmentation across different inputs.Inception-V4 performs initial classification; UNet++ with VGG19 handles 2.5D data, and Edge UNet handles grayscale data.Source: §1 · pp. 1–2
- The proposed model automates GI-tract segmentation in MRI scans, aiming to reduce manual effort in radiotherapy planning.The target regions are the colon, small intestine, and stomach.Source: §1 · pp. 1–2
- The preprocessing uses 2.5D and grayscale processing to enhance adaptability and robustness across imaging scenarios.The paper presents these preprocessing approaches as a contribution alongside its integrated architectures.Source: §1 · pp. 1–2
- The authors present the model as an efficient and accurate clinical tool intended to streamline radiotherapy planning and address inter-observer variability.The introduction also describes potential implications for optimizing planning and improving patient outcomes.Source: §1 · p. 2
2. RELATED WORK
The related work surveys medical-image segmentation and GI-organ segmentation, while noting that prior approaches do not fully address GI-tract-specific challenges. The paper positions its integrated architectures and preprocessing as a tailored response.
2. RELATED WORK
- The review cites U-Net for biomedical segmentation [9], VGG for image recognition [10], and Inception improvements for computer vision [11].It also discusses Edge U-Net’s use of Holistically-Nested Edge Detection for capturing edge features [4].Source: §2 · p. 2
- The review also covers GI-organ segmentation challenges involving anatomical variation and pathology [8], alongside work on medical segmentation and U-Net [1, 2].It additionally includes studies on swarm intelligence [5], pedestrian intent [6], and skin-lesion classification [7].Source: §2 · p. 2
- Other cited topics include breast-cancer detection [14], face recognition [15], and Bruch’s membrane segmentation [16].The review further lists graph-learning studies for Alzheimer’s diagnosis and point-cloud classification [17, 18, 19], as well as OCT imaging work [20, 21, 22].Source: §2 · pp. 2–3
- Prior works are described as falling short on GI-tract-specific challenges, motivating a tailored approach that integrates architectures and preprocessing.The paper frames this combination as a way to leverage diverse models’ strengths for GI-tract image segmentation.Source: §2 · p. 3
3.1 Overall Architecture
The GI-Tract-Image-Segmentation model uses three pathways: an initial classification route and two segmentation routes. Their predictions are averaged to produce the combined delineation.
3.1 Overall Architecture
- The overall architecture has three distinct pathways with separate purposes.Figure 1 depicts the tri-path design.Source: §3.1 · p. 3, Figure 1 · p. 3
3.1.1 Inception-V4 Pathway
- Inception-V4 first classifies whether healthy colon, small intestine, or stomach regions are present; absent detections yield a blank mask and end segmentation.This pathway acts as an initial classification stage.Source: §3.1.1 · p. 3
3.1.2 2.5D U-Net++ Pathway
- The 2.5D pathway stacks three consecutive MRI slices and inputs them to UNet++ with a VGG19 encoder to capture detailed features.The stacked representation incorporates depth information.Source: §3.1.2 · p. 4
3.1.3 Grayscale Edge U-Net Pathway
- The grayscale pathway uses Edge U-Net with Holistically-Nested Edge Detection, and its prediction is averaged with the 2.5D and Inception-V4 outputs.The paper describes grayscale processing as a simple, efficient way to capture essential segmentation information.Source: §3.1.3 · p. 4
3.2 Data Preprocessing
The preprocessing pipeline uses spatial augmentation and a separate 2.5D process. These steps standardize images, diversify training data, and add inter-slice context to the model’s inputs.
3.2 Data Preprocessing
- The preprocessing pipeline comprises two processes intended to improve model adaptability and generalization.Figure 2 depicts the data-processing pipeline.Source: §3.2 · p. 4, Figure 2 · p. 4
3.2.1 Spatial Augmentation Process
- Spatial preprocessing resizes images to 320x384 pixels and applies flipping, rotation, elastic transformation, and coarse random dropout.The output is grayscale, reducing color-data dimensions for later analysis and training.Source: §3.2.1 · p. 4
3.2.2 Intensity Augmentation Process - Grayscale
- Grayscale images receive intensity adjustments after resizing to 320x384 pixels to increase sensitivity to pixel-intensity variation.The resulting images contribute intensity information and dataset diversity for model training.Source: §3.2.2 · p. 4
3.2.3 2.5D Image Processing
- The 2.5D process stacks three consecutive MRI slices, preserving flat features while adding spatial context between slices.The resulting images undergo the same augmentation operations to increase dataset diversity and improve training effectiveness.Source: §3.2.3 · p. 5
3.3 Model Architectures
The section describes three complementary architectures: Inception-V4 for initial classification, U-Net++ with a VGG19 encoder for segmentation, and Edge U-Net for edge-aware segmentation.
3.3.1 Inception-V4 for Initial Classification
- Inception-V4 uses an initial block with varied filter sizes and max pooling, plus batch normalization and residual connections, to prioritize accuracy while mitigating computational costs.The described filters are 1x1, 3x3, and 5x5, with a 2x2 max-pooling filter.Source: §3.3.1 · p. 5
3.3.2 U-Net++ with VGG19 Encoder
- U-Net++ uses VGG19 convolutional layers as its encoder and a decoder connected by skip connections to leverage high-resolution features for segmentation.Source: §3.3.2 · p. 5
3.3.3 Edge U-Net
- Edge U-Net replaces U-Net encoder convolutional blocks with MBconv blocks and adds skip connections to capture edge data from the input.HED generates multi-scale edge images, which are fused with final edge features after upsampling.Source: §3.3.3 · p. 5
3.4 Evaluation Metrics
The section defines overlap and spatial-distance metrics for segmentation, then combines them into a composite score.
3.4.1 Dice Coefficient (DC)
- The Dice Coefficient quantifies similarity between predicted and ground-truth masks through their overlap.The metric is described as providing an assessment of segmentation accuracy.Source: §3.4.1 · p. 6
3.4.2 3D Hausdorff Distance
- The 3D Hausdorff Distance measures the maximum separation between corresponding pixels in predicted and ground-truth 3D masks.It is used to assess spatial dissimilarity and discrepancies between the masks.Source: §3.4.2 · p. 6
3.4.3 Composite Score
- The composite score weights the Dice Coefficient at 0.4 and the 3D Hausdorff Distance at 0.6 to assess overlap and spatial dissimilarity together.Source: §3.4.3 · p. 6
4. EXPERIMENTAL RESULTS
Experiments evaluate GI-tract segmentation on grayscale and 2.5D datasets. Edge U-Net performs best for grayscale images, while U-Net++ with VGG19 performs best for 2.5D images.
4. EXPERIMENTAL RESULTS
- The experiments evaluate network models with diverse encoders on grayscale and 2.5D preprocessed datasets.Source: §4 · p. 6
4.1 Grayscale Image Segmentation
- Edge U-Net exhibits the most effective performance among the evaluated models for grayscale image segmentation.Source: §4.1 · p. 7, Table 1 · p. 7
4.2 2.5D Image Segmentation
- U-Net++ with VGG19 as encoder excels among the evaluated models for 2.5D image segmentation.Source: §4.2 · p. 7, Table 2 · p. 7
5. CONCLUSION
The paper presents automated GI-tract MRI segmentation for radiotherapy planning using multiple deep-learning architectures. Its reported results identify different top-performing models for grayscale and 2.5D inputs.
5. CONCLUSION
- The proposed model combines Inception-V4, U-Net++ with a VGG19 encoder, and Edge U-Net to automate GI-tract MRI segmentation for radiotherapy planning.Source: §5 · p. 7
- Edge U-Net performs exceptionally well on grayscale images, while U-Net++ with VGG19 excels on 2.5D images.The paper describes these results as demonstrating the model’s effectiveness across different input types.Source: §5 · p. 7