Lung nodules segmentation from CT with DeepHealth toolkit
The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early detection of lung nodules is essential for the early cancer diagnosis and survival rate of patients. The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. Experiments for lung nodules segmentation has been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing as well as neural network training. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly available as a baseline reference.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Lung Nodules Detection and Segmentation Using 3D Mask-RCNN
Accurate assessment of Lung nodules is a time consuming and error prone ingredient of the radiologist interpretation work. Automating 3D volume detection and segmentation can improve workflow as well as patient care. Pre…
2D Object DetectionLung Nodule Detectionobject-detectionObject Detection+1Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Accurate lung lesion segmentation from Computed Tomography (CT) images is crucial to the analysis and diagnosis of lung diseases such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and th…
Computed Tomography (CT)Lesion SegmentationLung Nodule SegmentationSegmentation+1Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation
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 patie…
Computed Tomography (CT)Lung Nodule SegmentationTransfer LearningTransfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection
Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture wit…
Computed Tomography (CT)SegmentationTransfer LearningDeep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung
In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular…
ClassificationGeneral ClassificationSegmentationTexture Classification