College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.
Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, China.
Med Phys. 2024 May;51(5):3275-3291. doi: 10.1002/mp.17044. Epub 2024 Apr 3.
With the continuous development of deep learning algorithms in the field of medical images, models for medical image processing based on convolutional neural networks have made great progress. Since medical images of rectal tumors are characterized by specific morphological features and complex edges that differ from natural images, achieving good segmentation results often requires a higher level of enrichment through the utilization of semantic features.
The efficiency of feature extraction and utilization has been improved to some extent through enhanced hardware arithmetic and deeper networks in most models. However, problems still exist with detail loss and difficulty in feature extraction, arising from the extraction of high-level semantic features in deep networks.
In this work, a novel medical image segmentation model has been proposed for Magnetic Resonance Imaging (MRI) image segmentation of rectal tumors. The model constructs a backbone architecture based on the idea of jump-connected feature fusion and solves the problems of detail feature loss and low segmentation accuracy using three novel modules: Multi-scale Feature Retention (MFR), Multi-branch Cross-channel Attention (MCA), and Coordinate Attention (CA).
Compared with existing methods, our proposed model is able to segment the tumor region more effectively, achieving 97.4% and 94.9% in Dice and mIoU metrics, respectively, exhibiting excellent segmentation performance and computational speed.
Our proposed model has improved the accuracy of both lesion region and tumor edge segmentation. In particular, the determination of the lesion region can help doctors identify the tumor location in clinical diagnosis, and the accurate segmentation of the tumor edge can assist doctors in judging the necessity and feasibility of surgery.
随着深度学习算法在医学图像领域的不断发展，基于卷积神经网络的医学图像处理模型取得了很大的进展。由于直肠肿瘤的医学图像具有特定的形态特征和复杂的边缘，与自然图像不同，因此要获得良好的分割结果，通常需要通过利用语义特征来进行更高层次的特征丰富。
大多数模型通过增强硬件算法和更深的网络，在一定程度上提高了特征提取和利用的效率。然而，由于在深度网络中提取高层语义特征，仍然存在细节信息丢失和特征提取困难的问题。
本研究提出了一种新的医学图像分割模型，用于直肠肿瘤的磁共振成像（MRI）图像分割。该模型基于跳跃连接特征融合的思想构建骨干架构，使用三个新颖的模块（多尺度特征保留模块（MFR）、多分支交叉通道注意力模块（MCA）和坐标注意力模块（CA））解决了细节特征丢失和低分割精度的问题。
与现有的方法相比，我们提出的模型能够更有效地分割肿瘤区域，在 Dice 和 mIoU 度量上分别达到 97.4%和 94.9%，表现出优异的分割性能和计算速度。
我们提出的模型提高了病变区域和肿瘤边缘分割的准确性。特别是病变区域的确定可以帮助医生在临床诊断中识别肿瘤位置，肿瘤边缘的准确分割可以帮助医生判断手术的必要性和可行性。
