Debnath Ripon Kumar, Rahman Md Abdur, Azam Sami, Zhang Yan, Jonkman Mirjam
Department of Computer Science and Engineering, United International University, Dhaka, 1212, Bangladesh.
Faculty of Science and Technology, Charles Darwin University, Northern Territory, 0909, Darwin, Australia.
J Cancer Res Clin Oncol. 2025 Jul 17;151(7):215. doi: 10.1007/s00432-025-06256-0.
Precise liver segmentation is critical for accurate diagnosis and effective treatment planning, serving as a foundation for medical image analysis. However, existing methods struggle with limited labeled data, poor generalizability, and insufficient integration of anatomical and clinical features. To address these limitations, we propose a novel Few-Shot Segmentation model with Unified Liver Representation (FSS-ULivR), which employs a ResNet-based encoder enhanced with Squeeze-and-Excitation modules to improve feature learning, an enhanced prototype module that utilizes a transformer block and channel attention for dynamic feature refinement, and a decoder with improved attention gates and residual refinement strategies to recover spatial details from encoder skip connections. Through extensive experiments, our FSS-ULivR model achieved an outstanding Dice coefficient of 98.94%, Intersection over Union (IoU) of 97.44% and a specificity of 93.78% on the Liver Tumor Segmentation Challenge dataset. Cross-dataset evaluations further demonstrated its generalizability, with Dice scores of 95.43%, 92.98%, 90.72%, and 94.05% on 3DIRCADB01, Colorectal Liver Metastases, Computed Tomography Organs (CT-ORG), and Medical Segmentation Decathlon Task 3: Liver datasets, respectively. In multi-organ segmentation on CT-ORG, it delivered Dice scores ranging from 85.93% to 94.26% across bladder, bones, kidneys, and lungs. For brain tumor segmentation on BraTS 2019 and 2020 datasets, average Dice scores were 90.64% and 89.36% across whole tumor, tumor core, and enhancing tumor regions. These results emphasize the clinical importance of our model by demonstrating its ability to deliver precise and reliable segmentation through artificial intelligence techniques and engineering solutions, even in scenarios with scarce annotated data.
精确的肝脏分割对于准确诊断和有效的治疗规划至关重要，是医学图像分析的基础。然而，现有方法存在标记数据有限、泛化性差以及解剖和临床特征整合不足等问题。为了解决这些局限性，我们提出了一种具有统一肝脏表示的新型少样本分割模型（FSS-ULivR），该模型采用基于ResNet的编码器，并通过挤压激励模块进行增强以改善特征学习；一个增强的原型模块，利用Transformer块和通道注意力进行动态特征细化；以及一个解码器，采用改进的注意力门控和残差细化策略，从编码器跳跃连接中恢复空间细节。通过大量实验，我们的FSS-ULivR模型在肝脏肿瘤分割挑战数据集上取得了出色的Dice系数98.94%、交并比（IoU）97.44%和特异性93.78%。跨数据集评估进一步证明了其泛化性，在3DIRCADB01、结直肠癌肝转移、计算机断层扫描器官（CT-ORG）和医学分割十项全能任务3：肝脏数据集上的Dice分数分别为95.43%、92.98%、90.72%和94.05%。在CT-ORG上的多器官分割中，它在膀胱、骨骼、肾脏和肺部的Dice分数范围为85.93%至94.26%。对于BraTS 2019和2020数据集上的脑肿瘤分割，在整个肿瘤、肿瘤核心和增强肿瘤区域的平均Dice分数分别为90.64%和89.36%。这些结果通过展示其即使在注释数据稀缺的情况下也能通过人工智能技术和工程解决方案实现精确可靠分割的能力，强调了我们模型的临床重要性。
