如何计算医学图像分割中的TN指标？

在医学图像分割中，评估模型性能的指标非常重要。除了常用的 Dice 系数和 Jaccard 系数外，真阴性 (TN) 也是一个重要的指标。TN 表示模型正确预测为负例的样本数。

本文将介绍如何在 Python 代码中计算 TN 指标，并提供完整的代码示例。

代码示例

以下代码演示了如何计算医学图像分割任务中的 TN 指标。这段代码基于你提供的代码片段，并添加了 TN 的计算。pythondef eval_net(net, dataset, slicetotal, batch_size=12, gpu=True): '''Evaluation without the densecrf with the dice coefficient'''

net.eval()    start = time.time()    dice_ = torch.zeros(14).cuda()    jac_ = torch.zeros(14).cuda()    NE = torch.zeros(14).cuda()    JNE = torch.zeros(14).cuda()

accuracy_ = torch.zeros(14).cuda()    precision_ = torch.zeros(14).cuda()    recall_ = torch.zeros(14).cuda()    specificity_ = torch.zeros(14).cuda()

# 初始化 TN 数组    TN = torch.zeros(14).cuda()

print(1)    with torch.no_grad():        for i, b in enumerate(batch(dataset, batch_size)):

        imgs = np.array([k[0] for k in b]).astype(np.float32)            true_masks = np.array([k[1] for k in b])

        imgs = torch.from_numpy(imgs)            imgs = imgs.unsqueeze(1)            true_masks = torch.from_numpy(true_masks)

        pre_masks_eval = torch.zeros(true_masks.shape[0],14,256,256)            true_masks_eval = torch.zeros(true_masks.shape[0],14,256,256)            batchshape = true_masks.shape[0]

        batch_dice = torch.zeros(14).cuda()            if gpu:                imgs = imgs.cuda()                true_masks = true_masks.cuda()                net.cuda()

        output_img = net(imgs)            input = output_img.cuda()            pre_masks = input.max(1)[1].float() #索引代表像素所属类别的数字            for ak in range(14):                if ak == 0:                    continue                pre_masks_eval[:,ak] = (pre_masks==ak)                true_masks_eval[:,ak] = (true_masks==ak)                premasks = pre_masks_eval[:,ak].view(true_masks.shape[0],-1)                truemasks = true_masks_eval[:,ak].view(true_masks.shape[0],-1)

            intersection = premasks * truemasks                TP = intersection.sum(1)                FP = premasks.sum(1) - TP                FN = truemasks.sum(1) - TP

            # 计算 TN                TN[ak] += slicetotal - TP[ak] - FP[ak] - FN[ak]

            for bk in range(true_masks.shape[0]):                    if TP[bk] == 0 and FP[bk] == 0 and FN[bk] == 0:                        NE[ak] += 1                        JNE[ak] += 1                    else:                        batch_dice[ak] = batch_dice[ak] + 2*TP[bk] / (2*TP[bk] + FP[bk] + FN[bk])                        jac_[ak] = jac_[ak] + TP[bk] / (TP[bk] + FP[bk] + FN[bk])

        dice_ = dice_ + batch_dice

    for knum in range(14):            dice_[knum] = dice_[knum] / (slicetotal - NE[knum])            jac_[knum] = jac_[knum] / (slicetotal - JNE[knum])    end = time.time()    print('time used:',end - start)

return dice_, jac_,