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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2208.00641 (eess)
[Submitted on 1 Aug 2022]

Title:Lung nodules segmentation from CT with DeepHealth toolkit

Authors:Hafiza Ayesha Hoor Chaudhry, Riccardo Renzulli, Daniele Perlo, Francesca Santinelli, Stefano Tibaldi, Carmen Cristiano, Marco Grosso, Attilio Fiandrotti, Maurizio Lucenteforte, Davide Cavagnino
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Abstract: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.
Comments: Workshop ICIAP 2021 - Deep-Learning and High Performance Computing to Boost Biomedical Applications
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2208.00641 [eess.IV]
  (or arXiv:2208.00641v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2208.00641

Submission history

From: Daniele Perlo [view email]
[v1] Mon, 1 Aug 2022 06:54:12 UTC (344 KB)
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