Splenomegaly, or spleen enlargement, arises from a wide range of diseases and can lead to cytopenia, abdominal discomfort, and immune dysfunction. Magnetic resonance imaging (MRI) is commonly used to assess splenomegaly, providing detailed, radiation-free visualization of spleen size and structure. However, existing segmentation methods often show limited accuracy on MRI scans of enlarged spleens, as they are typically trained on cases with normal spleen sizes. This study aims to enhance spleen and neighboring organ segmentation accuracy in MRI scans by incorporating a small set of labeled splenomegaly cases into training. We propose an improved method, MRISegmenter++ (MS++), based on the nnU-Net segmentation framework, and benchmarked its performance against four established multi-organ segmentation methods: MRISegmenter (MS), MRAnnotator (MA), TotalSegmentator MRI (TS), and TotalVibeSegmentator (TV). On both internal and external datasets, MS++ performed comparably to MS, TS, and TV ( \(p >0.8\) ) on scans without splenomegaly, while significantly outperforming them on dedicated splenomegaly datasets ( \(p < .05\) ). These findings suggest that MS++ not only maintains high segmentation accuracy in patients with normal spleen size but also substantially improves performance in cases of splenomegaly. Accurate spleen segmentation may contribute to improved diagnosis and prognosis of splenomegaly in clinical practice.