Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation

• URL: http://arxiv.org/abs/2410.20154v1
• Date: Sat, 26 Oct 2024 11:58:12 GMT
• Title: Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation
• Authors: Jiasen Zhang, Mingrui Yang, Weihong Guo, Brian A. Xavier, Michael Bolen, Xiaojuan Li,
• Abstract summary: Lung cancer ranks as one of the leading causes of cancer diagnosis and is the foremost cause of cancer-related mortality worldwide. The segmentation of lung nodules plays a critical role in aiding physicians in distinguishing between malignant and benign lesions. We introduce a novel model for segmenting lung nodules in computed tomography (CT) images, leveraging a deep learning framework that integrates segmentation and classification processes.
• Score: 2.4044422838107438
• License:
• Abstract: Lung cancer ranks as one of the leading causes of cancer diagnosis and is the foremost cause of cancer-related mortality worldwide. The early detection of lung nodules plays a pivotal role in improving outcomes for patients, as it enables timely and effective treatment interventions. The segmentation of lung nodules plays a critical role in aiding physicians in distinguishing between malignant and benign lesions. However, this task remains challenging due to the substantial variation in the shapes and sizes of lung nodules, and their frequent proximity to lung tissues, which complicates clear delineation. In this study, we introduce a novel model for segmenting lung nodules in computed tomography (CT) images, leveraging a deep learning framework that integrates segmentation and classification processes. This model is distinguished by its use of feature combination blocks, which facilitate the sharing of information between the segmentation and classification components. Additionally, we employ the classification outcomes as priors to refine the size estimation of the predicted nodules, integrating these with a spatial regularization technique to enhance precision. Furthermore, recognizing the challenges posed by limited training datasets, we have developed an optimal transfer learning strategy that freezes certain layers to further improve performance. The results show that our proposed model can capture the target nodules more accurately compared to other commonly used models. By applying transfer learning, the performance can be further improved, achieving a sensitivity score of 0.885 and a Dice score of 0.814.

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