Esophageal Tumor Segmentation in CT Images using a 3D Convolutional Neural Network

by   Sahar Yousefi, et al.

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with , feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 ± 0.20, a mean surface distance of 5.4 ± 20.2mm and 95% Hausdorff distance of 14.7 ± 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via <>.


page 1

page 2

page 9

page 10


Automatic identification of segmentation errors for radiotherapy using geometric learning

Automatic segmentation of organs-at-risk (OARs) in CT scans using convol...

AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net

Tumor segmentation in medical imaging is crucial and relies on precise d...

Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Multimodal positron emission tomography-computed tomography (PET-CT) is ...

Technical report: Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter

Each year, there are about 400'000 new cases of kidney cancer worldwide ...

Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning

Distant metastases (DM) refer to the dissemination of tumors, usually, b...

ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network

Positron Emission Tomography (PET) is an imaging method that can assess ...

Please sign up or login with your details

Forgot password? Click here to reset