  Accurate segmentation of the Left Ventricle (LV) holds substantial importance
due to its implications in disease detection, regional analysis, and the
development of complex models for cardiac surgical planning. CMR is a golden
standard for diagnosis of serveral cardiac diseases. LV in CMR comprises of
three distinct sections: Basal, Mid-Ventricle, and Apical. This research
focuses on the precise segmentation of the LV from Cardiac MRI (CMR) scans,
joining with the capabilities of Machine Learning (ML). The central challenge
in this research revolves around the absence of a set of parameters applicable
to all three types of LV slices. Parameters optimized for basal slices often
fall short when applied to mid-ventricular and apical slices, and vice versa.
To handle this issue, a new method is proposed to enhance LV segmentation. The
proposed method involves using distinct sets of parameters for each type of
slice, resulting in a two-phase segmentation approach. The initial phase
categorizes images into three groups based on the type of LV slice, while the
second phase aims to segment CMR images using parameters derived from the
preceding phase. A publicly available dataset (Automated Cardiac Diagnosis
Challenge (ACDC)) is used. 10-Fold Cross Validation is used and it achieved a
mean score of 0.9228. Comprehensive testing indicates that the best parameter
set for a particular type of slice does not perform adequately for the other
slice types. All results show that the proposed approach fills a critical void
in parameter standardization through a two-phase segmentation model for the LV,
aiming to not only improve the accuracy of cardiac image analysis but also
contribute advancements to the field of LV segmentation.
