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A Computer-aided Framework for Detecting Osteosarcoma in Computed Tomography Scans

Maximo Rodriguez-Herrero, Dante D. Sanchez-Gallegos, Marco Antonio Núñez-Gaona, Heriberto Aguirre-Meneses, Luis Alberto Villalvazo Gutiérrez, Mario Ibrahin Gutiérrez Velasco, J. L. Gonzalez-Compean, Jesus Carretero

Abstract

Osteosarcoma is the most common primary bone cancer, mainly affecting the youngest and oldest populations. Its detection at early stages is crucial to reduce the probability of developing bone metastasis. In this context, accurate and fast diagnosis is essential to help physicians during the prognosis process. The research goal is to automate the diagnosis of osteosarcoma through a pipeline that includes the preprocessing, detection, postprocessing, and visualization of computed tomography (CT) scans. Thus, this paper presents a machine learning and visualization framework for classifying CT scans using different convolutional neural network (CNN) models. Preprocessing includes data augmentation and identification of the region of interest in scans. Post-processing includes data visualization to render a 3D bone model that highlights the affected area. An evaluation on 12 patients revealed the effectiveness of our framework, obtaining an area under the curve (AUC) of 94.8\% and a specificity of 94.6\%.

A Computer-aided Framework for Detecting Osteosarcoma in Computed Tomography Scans

Abstract

Osteosarcoma is the most common primary bone cancer, mainly affecting the youngest and oldest populations. Its detection at early stages is crucial to reduce the probability of developing bone metastasis. In this context, accurate and fast diagnosis is essential to help physicians during the prognosis process. The research goal is to automate the diagnosis of osteosarcoma through a pipeline that includes the preprocessing, detection, postprocessing, and visualization of computed tomography (CT) scans. Thus, this paper presents a machine learning and visualization framework for classifying CT scans using different convolutional neural network (CNN) models. Preprocessing includes data augmentation and identification of the region of interest in scans. Post-processing includes data visualization to render a 3D bone model that highlights the affected area. An evaluation on 12 patients revealed the effectiveness of our framework, obtaining an area under the curve (AUC) of 94.8\% and a specificity of 94.6\%.
Paper Structure (21 sections, 2 equations, 4 figures, 3 tables)

This paper contains 21 sections, 2 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Methodology
  4. Proposed Framework
  5. Dataset Construction
  6. Data Pre-processing
  7. Embedding-Based Misleading Label Detection
  8. Data Augmentations
  9. Tumor Classification
  10. Base CNN Determination
  11. Transfer Learning
  12. Generation of 3D Models for Data Visualization
  13. 3D Bone Model
  14. Affected Region Post-processing
  15. Experiments and Results
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Proposed framework for identifying osteosarcoma using CT images.
  • Figure 2: Automated pre-processing pipeline applied to each patient's CT scan. From left to right: raw CT image with scanning table artifacts, result of morphological opening using a disk-shaped structuring element (radius = 10px), binary mask obtained via Otsu thresholding, and final selection of the two largest connected components representing the leg region.
  • Figure 3: Slice-wise bone extraction pipeline applied to pre-processed CT images. From left to right: Gaussian smoothing ($\sigma=2$), K-means clustering with $k=5$, selection of the brightest cluster to isolate bone tissue, and final binary mask after morphological closing and hole filling outlined on the pre-processed CT scan.
  • Figure 4: 3D model output of the framework for an osteosarcoma-positive patient, shown from two different viewing angles. The red bounding box delineates the predicted affected region.