MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

doi:10.1109/ACCESS.2020.2971391

Return

MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

2020-01-01

Terapap Apiparakoon

Nutthaphol Rakratchatakul

Maythinee Chantadisai

Usanee Vutrapongwatana

Kanaungnit Kingpetch

Sasitorn Sirisalipoch

Yothin Rakvongthai

Tawatchai Chaiwatanarat

Ekapol Chuangsuwanich *

DOI：10.1109/ACCESS.2020.2971391

Original

Save

View PDF

Abstract

En 中文

One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and ground-truth or gold standard information from surgical or pathological reports may not be available. We propose a novel neural network model that can segment abnormal hotspots and classify bone cancer metastases in the chest area in a semisupervised manner. Our proposed model, called MaligNet, is an instance segmentation model that incorporates ladder networks to harness both labeled and unlabeled data. Unlike deep learning segmentation models that classify each instance independently, MaligNet utilizes global information via an additional connection from the core network. To evaluate the performance of our model, we created a dataset for bone lesion instance segmentation using labeled and unlabeled example data from 544 and 9,280 patients, respectively. Our proposed model achieved mean precision, mean sensitivity, and mean F1-score of 0.852, 0.856, and 0.848, respectively, and outperformed the baseline mask region-based convolutional neural network (Mask R-CNN) by 3.92%. Further analysis showed that incorporating global information also helps the model classify specific instances that require information from other regions. On the metastasis classification task, our model achieves a sensitivity of 0.657 and a specificity of 0.857, demonstrating its great potential for automated diagnosis using bone scintigraphy in clinical practice.

Keywords:

Bone scintigraphy

semi-supervised learning

lesion instance segmentation

deep learning

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.