# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
## 3 Experiments  
### Dataset and Image Processing  
We selected three different 3D BVS datasets that have varieties in the target organ, vessel size, and modality. Detailed information is described in Table 1.  
The first dataset is a brain time-of-flight (TOF) MRA for intracranial artery segmentation provided by Seoul National University Bundang Hospital (SNUBH). Twenty-six TOF MRA studies of normal cases were randomly selected, and only axial slices were downloaded from the picture archiving and communication system of SNUBH. The dataset was split into 18, 4, and 4 cases for the training, validation, and test set, respectively. Each case varied in the machine vendor, slice number per volume, and image size. The target vessel to segment was defined to all visible intracranial arteries except for the peripheral middle cerebral arteries after the M1 branch. The annotation of the groundtruth mask was done by two invited radiologists using the medical imaging interaction toolkit (version 2018.04.2). Because annotation from scratch takes a lot of time and human resources, we worked on the initial segmentation masks produced by a simple thresholding method that marks bright pixels with the intensity of the top 30% as blood vessel and the others as background. All images were normalized from 0 to 1, cropped centrally leaving 75% of the original image, and resized to 256 \(\times\) 256 pixels.  
The second dataset is an abdomen CT for hepatic vessel segmentation. We used a part of the publicly available Decathlon Challenge datasets [24] that contain 303 abdomen CT cases with annotation masks of liver tumor and hepatic vessel per axial slice. We split the dataset into 253, 25, and 25 cases for the training, validation, and test set, respectively. The image size was 512 \(\times\) 512 pixels, and the slice number was diverse. The tumor masks were ignored because our region-of-interest was only the hepatic vessel. For pre-processing, we clipped the Hounsfield Unit (HU) between 0 to 300 to eliminate a vast range of unnecessary HU. All images were normalized from 0 to 1, and 256 \(\times\) 256 pixels, centered on the liver area, were cropped and used.  
The last dataset was the public cardiac MRI dataset for left ventricle (LV) segmentation provided by York university [25]. Although LV is technically a type of organ rather than vessel, it conveys blood, which forms the definite inter-slice connectivity. The experiment on this dataset was used to validate the applicability of Spider U-Net to general organ segmentation with the inter-slice connectivity. For the given 33 MRI cases, each case had a single heartbeat composed of 3D cardiac volume with 20 frames. We split the dataset into 27, 3, and 3 cases for the training, validation, and test set, respectively. The given labels were initially two oval contours representing the endocardium and epicardium of the LV with 32 coordinates per slice. Slices not containing both contours were eliminated  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Target Vessel** & **Organizer** & \multicolumn{3}{c}{**\#Volumes**} & \multicolumn{1}{c}{**\#Slices Per Volume**} \\ \cline{3-6}  & & & **Train** & **Validation** & **Test** & \\ \hline Brain MRA & Intracranial Artery & SNUBH & 18 & 4 & 4 & 140–148 \\ Abdomen CT & Hepatic Vessel & Decathlon [24] & 253 & 25 & 25 & 24–181 \\ Cardiac MRI & Left Ventricle Wall & York Univ. [25] & 27 & 3 & 3 & 8–15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of 3D blood vessel segmentation datasets used in experiments.  
because we extracted the donut-shaped LV wall area between two labels and used it as a groundtruth mask. All images were normalized from 0 to 1.