2 research outputs found
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)
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 \mbox{air
pockets}, 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 value of ,
a mean surface distance of and Hausdorff distance of
for 287 test scans, demonstrating promising results with a
simplified clinical workflow based on CT alone. Our code is publicly available
via \url{https://github.com/yousefis/DenseUnet_Esophagus_Segmentation}
Automatic Detection of Air Holes inside the Esophagus in CT Images
Abstract. Air holes inside the esophagus can be used to localize the esophagus in computed tomographic (CT) images. In this work we present a technique to automatically detect esophageal air holes in this modality. Our technique is based on the extraction of a volume of interest, air segmentation by thresholding and classification of respiratory and esophageal air using a priori knowledge about the connectivity of air voxels. A post-processing step rejects wrong results from artifacts in the CT image. We successfully tested our algorithm with clinical data and compared the detection results of a human expert and our technique.