Abstract
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule’s boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system’s loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.
Highlights
Lung cancer is the most fatal cancer type in both men and women[1]
Several automatic lung nodule segmentation methods have been proposed with the goal of automating lung cancer screening
Convolutional Neural Networks (CNNs) have become the standard approach for medical image segmentation since they allow to significantly reduce the required field-knowledge and the need for manual feature design
Summary
Lung cancer is the most fatal cancer type in both men and women[1]. Thankfully, early diagnosis of this pathology and proper medical follow-up allow to increase the patients’ survival rate. A common solution is to adapt 3D U-Net[13] architectures, since they allow to consider both local and global context With this in mind, Wu et al.[14] proposed a multi-task scheme for pulmonary nodule segmentation together with the prediction of the nodules’ expected malignancy, achieving state-of-the-art performance in both tasks. Despite the high performance of deep learning methods, their application in the medical field is being criticized due to (1) the inherent lack of explanations behind the decision and, (2) the production of deterministic outputs, ignoring the existing inter-observer variability of the annotations and inhibiting the medical specialist to interact and change the decisions of the system With this in mind, Kohl et al.[15] proposed to model the inter-observer variability by combining a conditional variational auto encoder (cVAE) with an U-Net. The cVAE is used for drawing a set of feature maps sampled from the trained latent space representation. The method of Kohl et al does not allow the clinician to alter the segmentation, instead forcing the specialist to opt for the result closer to his/her expectations
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