Segmenting acute and hyper-acute brain lesions in neonatal hypoxic ischemic encephalopathy (HIE) from diffusion-weighted MRI (DWI) is critical for prognosis and treatment planning but remains challenging due to severe class imbalance and lesion variability. We propose a computationally efficient 2D segmentation framework leveraging ADC and ZADC maps as a three-channel input to UNet++ with an Inception-v4 encoder and scSE attention for enhanced spatial-channel recalibration. To address critical class imbalance and lack of volumetric context in 2D methods, we introduce a novel boundary-and-region-aware weighted loss integrating Tversky, Log-Hausdorff, and Focal losses. Our method surpasses state-of-the-art 2D approaches and achieves competitive performance against computationally intensive 3D architectures, securing a DSC of 0.6060, MASD of 2.6484, and NSD of 0.7477. These results establish a new benchmark for neonatal HIE lesion segmentation, demonstrating superior detection of both acute and hyper-acute lesions while mitigating the challenge of loss collapse. The code is available at https://github.com/BONBID-HIE/Neonatal-HIE-SPARSeg .