A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas
Artikel i vetenskaplig tidskrift, 2022

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance.

brain tumors

MR images

2D ellipse box areas

glioma segmentation

deep learning

multi-stream U-Net

Författare

Muhaddisa Barat Ali

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Xiaohan Bai

Student vid Chalmers

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Mitchel S. Berger

University of California at San Francisco

Asgeir Store Jakola

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Sensors

14248220 (eISSN)

Vol. 22 14

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Annan geovetenskap och miljövetenskap

Atom- och molekylfysik och optik

Biomedicinsk laboratorievetenskap/teknologi

DOI

10.3390/s22145292

PubMed

35890972

Mer information

Senast uppdaterat

2022-08-08