A Probabilistic Model for Segmentation of Ambiguous 3D Lung Nodule

Abstract

Many medical images domains suffer from inherent ambiguities. A feasible approach to resolve the ambiguity of lung nodule in the segmentation task is to learn a distribution over segmentations based on a given 2D lung nodule image. Whereas lung nodule with 3D structure contains dense 3D spatial information, which is obviously helpful for resolving the ambiguity of lung nodule, but so far no one has studied it.To this end we propose a probabilistic generative segmentation model consisting of a V-Net and a conditional variational autoencoder. The proposed model obtains the 3D spatial information of lung nodule with V-Net to learn a density model over segmentations. It is capable of efficiently producing multiple plausible semantic lung nodule segmentation hypotheses to assist radiologists in making further diagnosis to resolve the present ambiguity. We evaluate our method on publicly available LIDC-IDRI dataset and achieves a new state-of-theart result with 0.231±0.005 in D 2 GED . This result demonstrates the effectiveness and importance of leveraging the 3D spatial information of lung nodule for such problems.

Accurate classification of lung nodules on CT images using the TransUnet

“…We used the LIDI-IDRI database in this research; it contained 1,018 patients and is a widely web-accessible resource for evaluating lung cancer classification methods (16)(17)(18). Multiple clinical thoracic CT scan images and an XML file were included with each case.…”