Automatic segmentation of whole heart

Congenital Disease (CD) are defects that exist in newborn babies. Neural tube defects, craniofacial
anomalies, congenital heart diseases (CHD) are some of them and amongst them, Congenital Heart
Diseases are the most common type of anomalies that aect 4 to 50 per 1000 infants based on the
dierence in demographic characteristics and experiment conditions [1].
Medical Image segmentation is one of the most important parts of planning the steps of treatment
for patients with CHD. Image segmentation techniques aim to detect boundaries within a 2D or 3D
image and partition the image into meaningful parts based on pixel level information e.g. intensity
value and spatial information e.g. anatomical knowledge [3]. However, segmentation for a single 3D
medical image might take some hours. In addition to that, the complexity of images and the fact that
understanding these images needs medical expertise make them costly to annotate which makes an
automatic segmentation framework crucial.
Previously an interactive segmentation method is suggested for this purpose [2]. This master
thesis aims to reduce the manual interaction of the users by investigating dierent machine learning
approaches to nd a highly accurate model that could potentially replace the interactive solution.
The thesis has to comprise the following work items:
• Literature overview of state-of-the-art segmentation methods, particularly deep learning meth-
ods, for 3D medical images.
• Implementation and training of dierent deep learning segmentation models.
• Evaluation of trained models based on dice score and comparing them to previous interactive
approaches.
References
[1] Manuel Giraldo-Grueso, Ignacio Zarante, Alejandro Meja-Grueso, and Gloria Gracia. Risk factors
for congenital heart disease: A case-control study. Revista Colombiana de Cardiologa, 27(4):324{
329, 2020.
[2] Danielle F Pace. Image segmentation for highly variable anatomy: applications to congenital heart
disease. PhD thesis, Massachusetts Institute of Technology, 2020.
[3] Felix Renard, Soulaimane Guedria, Noel De Palma, and Nicolas Vuillerme. Variability and repro-
ducibility in deep learning for medical image segmentation. Scientic Reports, 10(1):1{16, 2020.