HTPSeg: A Semantic Segmentation Database for House-Tree-Person Psychological Test

The House Tree Person (HTP) test is widely recommended for clinical application of mental illness. Traditionally, therapists assess a patient's mental state by analyzing the content of their HTP drawings manually, which is time-consuming and susceptible to the therapist's subjective influences. Recently, using intelligent diagnostic models to tackle HTP test has attracted much attention. However, most existing models attempt to make a binary classification for HTP drawings, i.e., positive or negative psychological states, making details that can reflect the psychological state of the patient lost. To address the above challenge, in this paper, we introduce the semantic segmentation task into the HTP test for the first time to generate pixel-level semantic information. We first construct a semantic segmentation dataset about HTP psychological diagnosis named HTPSeg. Subsequently, to overcome the domain gap between HTP drawings and natural images, we propose to use the Low-Rank Adaptation (LoRA) fine-tuning strategy to adapt the Segment Anything Model (SAM) to the task of HTP drawing analysis. Specifically, we integrate the rank decomposition matrices into the projection layer of the transformer block in SAM's image encoder for fine-tuning. Additionally, we freeze the Mask Decoder and fine-tune the Prompt Encoder using default embeddings. Extensive experiments indicate the effectiveness and efficiency of the proposed method. The dataset and code will be available at https://github.com/clown06/HTPSeg.