Analysis of Segmentation and Modeling of Lung Cancer Images Scanned Continuously by Computed Tomography Based on Materiaise's Interactive Medical Image Control System

Pan, Hongtao; Dong, Mo; Zhao, Xiangkun; Zhou, Zhizun; Zhou, Hongsuo

doi:10.1166/jmihi.2020.2947

Cited by 1 publication

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“…Hongtao et al (2020) [ 27 ] demonstrated the application of segmentation and modeling of lung cancer using 3D renderings created from CT images. The segmentation performed using MIMICS17.0 software and demonstrated high precision; however, due to software limitations, the exact coordinates of tumor location cannot yet be exported.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

Šegota

Lorencin

Smolić

et al. 2021

Biology

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Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.

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Section: Introductionmentioning

confidence: 99%