TITLE: Challenges of building medical image datasets for development of deep learning software in stroke 

ABSTRACT: Despite the large amount of brain CT data generated in clinical practice, the
availability of CT datasets for deep learning (DL) research is currently
limited. Furthermore, the data can be insufficiently or improperly prepared for
machine learning and thus lead to spurious and irreproducible analyses. This
lack of access to comprehensive and diverse datasets poses a significant
challenge for the development of DL algorithms. In this work, we propose a
complete semi-automatic pipeline to address the challenges of preparing a
clinical brain CT dataset for DL analysis and describe the process of
standardising this heterogeneous dataset. Challenges include handling image
sets with different orientations (axial, sagittal, coronal), different image
types (to view soft tissues or bones) and dimensions, and removing redundant
background. The final pipeline was able to process 5,868/10,659 (45%) CT image
datasets. Reasons for rejection include non-axial data (n=1,920), bone
reformats (n=687), separated skull base/vault images (n=1,226), and
registration failures (n=465). Further format adjustments, including image
cropping, resizing and scaling are also needed for DL processing. Of the axial
scans that were not localisers, bone reformats or split brains, 5,868/6,333
(93%) were accepted, while the remaining 465 failed the registration process.
Appropriate preparation of medical imaging datasets for DL is a costly and
time-intensive process.
Despite the large amount of brain CT data generated in clinical practice, the
availability of CT datasets for deep learning (DL) research is currently
limited. Furthermore, the data can be insufficiently or improperly prepared for
machine learning and thus lead to spurious and irreproducible analyses. This
lack of access to comprehensive and diverse datasets poses a significant
challenge for the development of DL algorithms. In this work, we propose a
complete semi-automatic pipeline to address the challenges of preparing a
clinical brain CT dataset for DL analysis and describe the process of
standardising this heterogeneous dataset. Challenges include handling image
sets with different orientations (axial, sagittal, coronal), different image
types (to view soft tissues or bones) and dimensions, and removing redundant
background. The final pipeline was able to process 5,868/10,659 (45%) CT image
datasets. Reasons for rejection include non-axial data (n=1,920), bone
reformats (n=687), separated skull base/vault images (n=1,226), and
registration failures (n=465). Further format adjustments, including image
cropping, resizing and scaling are also needed for DL processing. Of the axial
scans that were not localisers, bone reformats or split brains, 5,868/6,333
(93%) were accepted, while the remaining 465 failed the registration process.
Appropriate preparation of medical imaging datasets for DL is a costly and
time-intensive process.
Challenges of building medical image datasets for development of deep learning software in stroke
http://arxiv.org/abs/2309.15081v1