  Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential
role in the early diagnosis and treatment of liver cancer. Deep learning models
backboned by fully convolutional neural networks (FCNNs) have become the
dominant model for segmenting 3D computerized tomography (CT) scans. However,
since their convolution layers suffer from limited kernel size, they are not
able to capture long-range dependencies and global context. To tackle this
restriction, vision transformers have been introduced to solve FCNN's locality
of receptive fields. Although transformers can capture long-range features,
their segmentation performance decreases with various tumor sizes due to the
model sensitivity to the input patch size. While finding an optimal patch size
improves the performance of vision transformer-based models on segmentation
tasks, it is a time-consuming and challenging procedure. This paper proposes a
technique to select the vision transformer's optimal input multi-resolution
image patch size based on the average volume size of metastasis lesions. We
further validated our suggested framework using a transfer-learning technique,
demonstrating that the highest Dice similarity coefficient (DSC) performance
was obtained by pre-training on training data with a larger tumour volume using
the suggested ideal patch size and then training with a smaller one. We
experimentally evaluate this idea through pre-training our model on a
multi-resolution public dataset. Our model showed consistent and improved
results when applied to our private multi-resolution mCRC dataset with a
smaller average tumor volume. This study lays the groundwork for optimizing
semantic segmentation of small objects using vision transformers. The
implementation source code is available
at:https://github.com/Ramtin-Mojtahedi/OVTPS.
