Gupta Pankaj, Dutta Niharika, Tomar Ajay, Singh Shravya, Choudhary Sonam, Mehta Nandita, Mehta Vansha, Sheth Rishabh, Srivastava Divyashree, Thanihai Salai, Singla Palki, Prakash Gaurav, Yadav Thakur, Kaman Lileswar, Irrinki Santosh, Singh Harjeet, Shah Niket, Choudhari Amit, Patkar Shraddha, Goel Mahesh, Yadav Rajnikant, Gupta Archana, Kumar Ishan, Seth Kajal, Dutta Usha, Arora Chetan
Post Graduate Institute of Medical Education and Research, Chandigarh, India.
Indian Institute of Technology, New Delhi, India.
Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04887-y.
To train and validate segmentation models for automated segmentation of gallbladder cancer (GBC) lesions from contrast-enhanced CT images.
This retrospective study comprised consecutive patients with pathologically proven treatment naïve GBC who underwent a contrast-enhanced CT scan at four different tertiary care referral hospitals. The training and validation cohort comprised CT scans of 317 patients (center 1). The internal test cohort comprised a temporally independent cohort (n = 29) from center 1 (internal test 1). The external test cohort comprised CT scans from three centers [ (n = 85)]. We trained the state-of-the-art 2D and 3D image segmentation models, SAM Adapter, MedSAM, 3D TransUNet, SAM-Med3D, and 3D-nnU-Net, for automated segmentation of the GBC. The models' performance for GBC segmentation on the test datasets was assessed via dice score and intersection over union (IoU) using manual segmentation as the reference standard.
The 2D models performed better than 3D models. Overall, MedSAM achieved the highest dice and IoU scores on both the internal [mean dice (SD) 0.776 (0.106) and mean IoU 0.653 (0.133)] and external [mean dice (SD) 0.763 (0.098) and mean IoU 0.637 (0.116)] test sets. Among the 3D models, TransUNet showed the best segmentation performance with mean dice (SD) and IoU (SD) of 0.479 (0.268) and 0.356 (0.235) in the internal test and 0.409 (0.339) and 0.317 (0.283) in the external test sets. The segmentation performance was not associated with GBC morphology. There was weak correlation between the dice/IoU and the size of the GBC lesions for any segmentation model.
We trained 2D and 3D GBC segmentation models on a large dataset and validated these models on external datasets. MedSAM, a 2D prompt-based foundational model, achieved the best segmentation performance.
训练并验证用于从增强CT图像中自动分割胆囊癌（GBC）病变的分割模型。
这项回顾性研究纳入了连续的初治GBC患者，这些患者在四家不同的三级医疗转诊医院接受了增强CT扫描，且病理诊断明确。训练和验证队列包括317例患者的CT扫描数据（中心1）。内部测试队列包括来自中心1的一个时间上独立的队列（n = 29）（内部测试1）。外部测试队列包括来自三个中心的CT扫描数据[（n = 85）]。我们训练了最先进的2D和3D图像分割模型，即SAM Adapter、MedSAM、3D TransUNet、SAM-Med3D和3D-nnU-Net，用于GBC的自动分割。以手动分割作为参考标准，通过骰子系数和交并比（IoU）评估模型在测试数据集上对GBC分割的性能。
2D模型的表现优于3D模型。总体而言，MedSAM在内部[平均骰子系数（标准差）0.776（0.106），平均IoU 0.653（0.133）]和外部[平均骰子系数（标准差）0.763（0.098），平均IoU 0.637（0.116）]测试集上均取得了最高的骰子系数和IoU分数。在3D模型中，TransUNet在内部测试中的分割性能最佳，平均骰子系数（标准差）和IoU（标准差）分别为0.479（0.268）和0.356（0.235），在外部测试集中分别为0.409（0.339）和0.317（0.283）。分割性能与GBC形态无关。对于任何分割模型，骰子系数/IoU与GBC病变大小之间的相关性较弱。
我们在一个大型数据集上训练了2D和3D GBC分割模型，并在外部数据集上对这些模型进行了验证。基于2D提示的基础模型MedSAM取得了最佳分割性能。
