LDLCT an instance-based framework for lesion detection on lung CT scans

Tarun Khajuria, Eman Badr, Mouaz Al-Mallah, Sherif Sakr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Medical images have played a crucial role in transforming diagnostic medicine by providing the medical staff with several insights into the health status of every patient. Diagnosis of medical images is a very subjective process which is solely based on the physicians' expertise. In particular, lesion detection is a challenging task due to the various types, shapes and sizes of the lesions in the different organs. Thus, there is a crucial need to build computer-aided frameworks for automated analysis of medical images. In this paper, we present LDLCT, an instance-based framework for automated Lesion Detection on Lung CT Scans. The framework employs Restricted Botlzman Machines (RBM) network for feature extraction stage as an unsupervised feature mapping. The Random Forest (RF) classifier is used to distinguish between pixels from lesion and normal regions. Finally, a post-processing stage is implemented to filter out the false positive candidate lesions. In this study, we select 909 slices with 917 lesions from DeepLesion data set. LDLCT achieves lesion detection sensitivity of 89% with 5 false positives per image, the majority of them can be easily detected by the medical staff.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages523-526
Number of pages4
Volume2019-June
ISBN (Electronic)9781728122861
DOIs
StatePublished - Jun 1 2019
Event32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Cordoba, Spain
Duration: Jun 5 2019Jun 7 2019

Other

Other32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
CountrySpain
CityCordoba
Period6/5/196/7/19

Keywords

  • Instance-based Segmentation
  • Lesion Detection
  • Random Forest
  • RBM

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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